| 00:00 | Okay. Hello, are we Hello are we back? Yeah. |
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| 00:09 | sorry. Yeah. I'm sorry. I'm I'm I'm three minutes late. |
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| 00:15 | sorry I just couldn't eat fast Sorry I'm sorry. We could have |
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| 00:21 | a longer lunch. No that's I don't care. Get it over |
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| 00:25 | you know, stop the pain as as possible. Alright. So are |
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| 00:33 | recording now? Yes I'm alright. . Are you still eating? You |
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| 00:43 | finish it? You you finish yours , you're a better man than |
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| 00:50 | Okay. Alright. So Cruising along # three this is um anomaly |
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| 01:00 | So this is now we're kind of a lot of the painful stuff behind |
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| 01:06 | . Now the sort of background You know we you know what financials |
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| 01:13 | . We know what fields are. know what kind of instruments they used |
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| 01:16 | measure them and how the data are and how to design surveys and corrections |
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| 01:23 | processing. Now we're getting to the where we can start to in for |
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| 01:32 | which is really the goal here, it? It's what we want to |
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| 01:35 | . So um let's let's move So anomaly enhancement. Um Let me |
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| 01:44 | a drink here. Okay so after do the standard processing the corrections, |
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| 01:55 | things that must be done before you start looking at the data with your |
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| 02:04 | geologic perspective. We recognize that The anomaly filled with uh is full |
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| 02:15 | composite signals. There are long wavelengths short wavelengths and short wavelengths superimposed on |
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| 02:23 | wavelengths. There are anomalies that constructively destructively interfere with one another. So |
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| 02:31 | are lots of lots of things still on and they're all related to |
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| 02:36 | So we want to come up with tools to isolate and identify these signals |
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| 02:43 | that we can one maybe correlate some with geology that we know very |
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| 02:55 | And this is just just an you know, this is a sort |
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| 03:01 | a geological inference. If I know it is here, is it the |
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| 03:07 | thing over there kind of idea? you you you might, you |
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| 03:14 | do some sort of enhancement of filter residual or something like that. And |
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| 03:22 | dad enhances something that you understand and what you want to maybe from there |
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| 03:30 | into areas where you trying to learn . That's the idea. That's one |
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| 03:35 | the many ideas. Okay, so there are long and two short wavelength |
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| 03:40 | aptitude, short waving to long wavelength amplitude, blah, blah blah. |
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| 03:45 | I said, there's all kinds of of the anomalies. So these are |
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| 03:51 | techniques we're gonna look at. You , two, we're going to apply |
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| 03:55 | to the anomalies. Any, any at enhancing some anomalies at the expense |
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| 04:04 | others. That's where this in this is what I'm using this term |
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| 04:11 | . And I think that there are , there are three main ways that |
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| 04:15 | do this. We generate regional So we generate a regional field subtract |
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| 04:22 | from the observed data and when I observed, I mean post process that's |
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| 04:27 | been leveled and all the questions So for enhancements were already dealing with |
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| 04:34 | data. So you take that your post process data and you calculate some |
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| 04:41 | field long wavelength failed and you subtract from the original and that will give |
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| 04:47 | some residual. Quite often. We're in shorter wavelengths and lower amplitudes than |
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| 04:57 | might be dominating the good level You can also do filters. So |
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| 05:04 | just you know, just basically for transform into the frequency domain and then |
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| 05:11 | know, band limited data very We can also do derivatives. So |
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| 05:18 | take, you know the change in , Y Z or altogether or some |
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| 05:23 | of the change in the field with to its position, horizontal derivatives, |
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| 05:31 | derivatives. Um We could do what call analytic signal which is a combination |
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| 05:40 | horizontal and vertical derivatives, things called derivatives, all kinds of stuff. |
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| 05:47 | people have, they're always coming up stuff like that. So as I |
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| 05:55 | , it's usually we wanna we wanna some long way that component to reveal |
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| 06:00 | that's that's shorter wave We can There's also a tool called wavelet transforms |
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| 06:07 | has wide application ability but I'm not people are doing a lot of it |
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| 06:16 | 2000 Maybe 1999 until like 2002 or people were doing a lot of wavelet |
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| 06:24 | but I think it's you know I I don't really see it around that |
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| 06:30 | anymore but maybe it's still being used lot. There's two things that we |
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| 06:35 | do the magnetic data. We can the danger of the pole. So |
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| 06:40 | other words we can do a phase such that the anomaly pair of the |
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| 06:45 | low pair can appear as though the were sitting over the pole one of |
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| 06:53 | pool. This is only with the reducing field and then we can do |
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| 07:01 | R. T. E. Reduced the equator. Now I I |
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| 07:04 | not a fan of either one of but I will teach them to |
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| 07:08 | I'll explain how they work. I explain why I don't I don't like |
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| 07:12 | . And explain to you why people like them. There's a process that's |
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| 07:16 | Kathleen pseudo gravity from magnetic data. that makes use of poisons race relations |
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| 07:27 | you can basically express. Uh You you can you can express the magnetic |
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| 07:36 | as though it's you what you do you assume that the anomalies are produced |
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| 07:42 | by density changes. You know? you have the magnetic anomalies that are |
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| 07:46 | by magnetic susceptibility variations. But you that they're produced by density variations. |
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| 07:55 | there's a way to do that calculation poisons relations. That can be a |
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| 08:02 | exercise and I'll explain the pros and of that to you as well. |
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| 08:11 | now my why academic advisor through college at the University of Houston, he |
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| 08:19 | a distinction between enhancement and separation. I don't necessarily see it that |
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| 08:27 | I understand he's talking about he's saying for example, there's different signals from |
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| 08:33 | sources that are producing anomalies. In words this this dyke is producing this |
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| 08:41 | . And if I can take a through that then I can remove that |
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| 08:47 | that's producing solely from that. But think that you have to know the |
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| 08:52 | to do that. And I don't you necessarily do. So I think |
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| 08:58 | is sort of a yeah, if have a model and a field produced |
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| 09:02 | a model. Yeah, I can that. But why do I care |
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| 09:07 | separating regionals, residuals from the model you know what the model is? |
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| 09:11 | mean this is yeah. So I really, you know, I don't |
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| 09:14 | um Yeah. Anyways I'm gonna get crosswise with him but I don't really |
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| 09:21 | it's showing much, I don't think much of a difference. It's a |
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| 09:24 | without a difference I think. but you will hear people talk say |
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| 09:31 | , I'm just telling you because you people if you you know if you |
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| 09:37 | not hear people but if you find in a you know at a cocktail |
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| 09:42 | talking to some potential field scientists and talking about anomaly separation versus enhancement. |
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| 09:48 | know what they're talking about? You okay so now there are things we |
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| 09:54 | do in the spatial domain and things can do in the frequency domain. |
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| 09:58 | so we're gonna talk about spatial domain and um um And yeah so those |
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| 10:09 | work directly on the nose that deal you know spatial frequencies, I don't |
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| 10:14 | I guess that they are two different . One is you know 48. |
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| 10:20 | You could do you know just you originally people would do these residuals just |
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| 10:25 | graphically tracing through and then just doing subtraction along that profile. So maybe |
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| 10:33 | this this this dash line might be regional field and this is your observed |
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| 10:39 | . So you should practice too. then you're gonna have you know what's |
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| 10:43 | over. Nettleton has a nice picture in his in in one of his |
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| 10:51 | that this one is 1971. So is really interesting because it shows how |
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| 10:57 | can do it differently. So the line, that's the measured data. |
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| 11:05 | um you could if you took a residual of this basically if you did |
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| 11:18 | band pass student you might end up something like this. We have highs |
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| 11:25 | lows and it's gonna be some thing if you did one graphically you just |
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| 11:31 | through here, you're gonna end up that look like this. So it's |
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| 11:38 | same data, it's just represented a bit different um Yeah, yeah, |
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| 11:51 | makes sense to you. Okay, the graphical method is subjective, I |
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| 11:56 | it's like, you know, what you want it to be? Just |
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| 12:00 | through where you think the regional should , you're just making it up as |
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| 12:04 | go along. So the grid method more objective, right? It's treating |
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| 12:10 | exactly the same. Um So uh the graphical method has more flexibility because |
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| 12:21 | permits you to, it allows you , you know, if you know |
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| 12:26 | some geology very well, then it you to actually, you know, |
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| 12:32 | put that information into the, into regional field and you know, so |
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| 12:41 | there are, there are good parts that even though it's hard work. |
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| 12:47 | here's a, here's a, this in Middleton's 1950 for book um here's |
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| 12:55 | here's an area just so here's Houston here, Houston's probably all the way |
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| 12:59 | here right now. This is South . So it didn't used to be |
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| 13:03 | of, you know, engulfed in Houston, but it's a it's the |
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| 13:10 | field that the observed data and you see there's this big ramp, this |
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| 13:15 | regional field through here which is contour is um is to Milligan's so if |
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| 13:25 | just trace this regional field going through , so the ramp goes from minus |
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| 13:32 | to 4. So it's a five Igel sort of ramp going down from |
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| 13:38 | to south. Good to see they have Pasadena separate. Then you can |
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| 13:47 | this from this and you get a field. This is calculated at 1/5 |
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| 13:52 | a nano Tesla 50 mg rather. you can see there's where this salt |
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| 13:58 | is down here, this big low this is just the too late on |
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| 14:04 | of each other here, regional contour one look out this, this is |
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| 14:13 | kind to milligrams one mg, fifth here they are just combined. So |
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| 14:18 | a graphical way of making a regional anomaly separation or residual enhancement if you |
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| 14:29 | . Now their spatial operate radios that can use to generate regional fields from |
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| 14:35 | data. And special operators are essentially . Right, so you're summing and |
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| 14:43 | sort of thing. Um the center where you're summing, summing and |
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| 14:49 | And so the value is considered regional at some center points you have a |
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| 14:57 | have a moving circle across your grid then it around the grid around that |
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| 15:07 | that calculates the value and it assigns value to the center, I'll show |
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| 15:14 | . So what's important here is the the radius of the circle. So |
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| 15:22 | radius are longer wavelengths of course small more detailed regionals. Alright, so |
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| 15:30 | does that, what does that look ? So here's a bunch of station |
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| 15:35 | , well, grid nodes with our on them. So they're ranging from |
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| 15:40 | over 400, So that's the these data values for spatially distributed uh point |
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| 15:53 | here's our operator, it's a four four. Uh It's a circle that |
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| 15:58 | a four by four grid nodes. sorry, that would be nine x |
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| 16:05 | rather. And so it has a point and it has, you |
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| 16:09 | these four points out. So you you were to calculate the regional of |
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| 16:13 | gina is equal to the sum of , divided by four and move along |
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| 16:18 | the same. So the residual would um the original day at each location |
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| 16:25 | would be that original value minus whatever regional component is. That will give |
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| 16:31 | the residual. So what does that like in practice? So they were |
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| 16:36 | were right here and we have um four values here. 3 43 74 |
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| 16:46 | 85 3 82. So we would these four outer ones, some those |
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| 16:53 | by four. And this would be value That we would subtract from |
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| 16:58 | And that would give us a residual 5.5. So it's very very, |
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| 17:02 | simple stuff. Right? And this the kind of thing that that |
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| 17:07 | These are old figures from Stewart So on the on the left, |
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| 17:18 | are the this is the regional this is the residual from them. |
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| 17:23 | you have a range of um december 52/4 50 where the residual is minus |
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| 17:31 | to about four. So you can others. So if you take a |
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| 17:38 | circle, well then you can do same thing but in this case now |
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| 17:45 | you're subtracting 9, 9.5 from the mean the value of the residual is |
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| 17:54 | . So here's the regional grid spacing here it is with route two of |
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| 17:58 | grid spacing. The radius is route the grid. So you can see |
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| 18:02 | it doesn't change that much in this . I'm sorry this is this is |
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| 18:08 | regional with the grid spacing and the with the other one. You can |
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| 18:16 | , you can do to service These are just old school stuff |
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| 18:21 | G The Small one G. A to. So then you would um |
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| 18:28 | know, combine those and maybe weight according to the to the distance, |
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| 18:33 | ? You would weight them according to big the circle is regarded is. |
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| 18:39 | , so and then there's tables where have worked out what weights you should |
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| 18:47 | all these different things according to that . Okay this this stuff can all |
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| 18:52 | is all computerized now but this is of like how it works. Okay |
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| 18:58 | we can calculate trends. There could as movable trends that in one direction |
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| 19:04 | some pervasive trend that's really dominating the that you want to sort of take |
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| 19:12 | . Now this can be perilous because could actually be removing geology if you're |
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| 19:16 | careful. So just saying um and trend surface, uh you loosely fit |
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| 19:25 | observed data to some mathematical function where trend is modeled from. And it |
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| 19:33 | be a bunch of different things, polynomial in X. And Y. |
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| 19:37 | something generated from, you know, approximation. And then you subtract this |
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| 19:44 | from the observed data to try to it, try to reduce it. |
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| 19:50 | So so so you're using polynomial trends higher the order, the closer it's |
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| 19:57 | to fit the data. But if fit it too much, you're just |
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| 20:02 | be reproducing the data. If you're if you don't do enough, it's |
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| 20:06 | going to remove like not enough, just gonna still have some wide way |
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| 20:11 | component. So all this stuff was little tricky. And I can tell |
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| 20:16 | from experience what in a place where don't really know a lot of what's |
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| 20:21 | on geologically. Sometimes the best thing do is just to make a suite |
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| 20:26 | enhancements. Do a bunch of band . Not only math, do a |
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| 20:30 | of residuals, do a bunch you know, maybe try and trend |
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| 20:34 | another one and just like stick them the wall and sit back and stare |
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| 20:39 | them for a while and then, know, you can start to draw |
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| 20:42 | conclusions on what you think is the way to enhance the data to help |
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| 20:49 | understand geology, the geology of the . So here's a study that was |
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| 20:56 | in another Middleton publication in 71 And in this case, so he |
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| 21:02 | this this is a bouquet of And here's the seventh order trend of |
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| 21:08 | . So you can see there is just booming through, not only here |
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| 21:14 | wants to minimize. So here it , here's the residual of that. |
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| 21:19 | if you take a look at here's the through this cross section |
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| 21:23 | that's going from northwest to southeast. the bouquet gravity, the dash line |
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| 21:30 | here's the trend is the trend to the seventh or polio. Oh |
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| 21:37 | here's a subtraction. So, so guess I don't know if that's, |
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| 21:45 | guess if you like that, you that, you see this one here |
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| 21:48 | going this is over 100 million And now he's less than that. |
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| 21:53 | , you know, he's enhanced these at the expense of this big giant |
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| 21:59 | . So everything is flat down here this anomaly is just totally swamping the |
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| 22:06 | . So he's just knocking this one um basically so that he can sort |
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| 22:12 | see what's going on next to Okay, So here's the same thing |
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| 22:17 | a 10th order polynomial. So this a higher order. So it's gonna |
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| 22:22 | fit fit the data even closer. then here's the residual from that. |
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| 22:30 | you see that now, it's almost it too well, right? Because |
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| 22:35 | almost completely removed this anomaly is just few little things in here. So |
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| 22:40 | fact, you know, is this this even an anomaly? So um |
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| 22:46 | have to be careful what you're doing this stuff. Is there one |
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| 22:49 | Just one more? Yeah. So he's doing, he's doing 1/13 order |
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| 22:55 | and he's really fitting the data and residual has all these really subtle features |
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| 23:00 | here. Now, the stations are dots. So some of these |
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| 23:05 | they're still probably real. They're just , very, very subtle. But |
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| 23:09 | your that's what your target is, you really want to find out what's |
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| 23:14 | even beneath this big anomaly, you know, that's a little small |
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| 23:21 | . Then you could do this, this is a really high order |
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| 23:25 | So anymore. No good. and then we can transform into the |
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| 23:33 | year the wave number domain. so for example, you know, |
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| 23:40 | uh you can just filter it you know, limiting it by wave |
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| 23:46 | . Right? So here's our original . And then when you look at |
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| 23:50 | amplitude amplitude spectrum, you have this thing. So this is the longer |
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| 23:56 | in the middle and shorter wavelengths around rim, that's how it works because |
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| 24:00 | like a it's like a radio radio average followers. I mean radio average |
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| 24:06 | spectrum. So uh where the amplitude rather. So it's longer wavelengths in |
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| 24:12 | very center. And as you radiate is shorter wavelengths. So say you |
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| 24:18 | to target the longer wavelengths so you you zap these longer wavelengths maybe along |
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| 24:23 | line, zap those. And when re plot it, you've got |
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| 24:28 | So now you've got these anomalies are enhanced and these these anomalies are as |
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| 24:35 | . So you're starting with here, ending with this. And so you |
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| 24:38 | superimposed on this gradient, this long gradient that's trending, you know, |
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| 24:44 | kind of like well the gradient trend that you have these this low and |
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| 24:50 | high that you can sort of So you hit it to you in |
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| 24:57 | spectrum. You hit it with this of trend through here, zaps all |
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| 25:02 | when you and then that's what it like after you kill those regional components |
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| 25:08 | when you transform back in verse you can see the residual. Does |
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| 25:14 | makes sense. That makes sense, . Hello, I'm sorry, I |
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| 25:25 | I couldn't find my button. I'm good. Makes your you're good |
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| 25:29 | this. Okay. All right, . Alright, so conversion between spatial |
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| 25:37 | is carried out especially being are equivalent a multiplication carried out in the frequency |
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| 25:42 | . Right and right. Convolution in is right. All good libraries have |
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| 25:47 | equivalent filter function in the frequency So. Right. No, no |
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| 25:54 | . With. So that's just a between the two. So we can |
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| 26:00 | limit data filters allow some signals to while blocking others of course. And |
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| 26:05 | you have some sort of tape. um in the frequency domain plot below |
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| 26:11 | pass filters include a high cut over and a low cut over here. |
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| 26:16 | cut off some cut off frequency that and this is the sort of, |
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| 26:22 | am not a fan of band limiting just because I think you can produce |
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| 26:27 | with it. I mean back in day the computer power was that good |
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| 26:37 | a was pretty useful especially in I mean of course useful for reflection |
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| 26:43 | because of those time series. But you look at your basic, your |
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| 26:50 | gravity magnetic anomaly map, you might have like two dozen anomalies on the |
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| 26:54 | math, you know, So there's no reason to like, I mean |
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| 26:59 | not like you have to, you , two million wavelengths to process. |
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| 27:03 | ? So it's a different and also space was not temporal. People do |
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| 27:09 | all the time. I I do once in a while. I mean |
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| 27:12 | not saying I don't ban limit I tend to do continuation residuals as |
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| 27:19 | think they're much nicer to the data you can ban limit data if you |
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| 27:25 | . So these are examples from Bill book 27, this is actually the |
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| 27:30 | recent text on gravity magnetic field sort a general textbook. So below our |
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| 27:42 | and observe and I'm going to use examples for many cases and I wish |
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| 27:48 | in color. I should maybe do in color. But these are from |
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| 27:52 | book and they're the same area and same data. So I'm just going |
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| 27:56 | use them. But so the the country was 1/5 of the middle |
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| 28:02 | and it looks like the range goes -2.4 - 1.5. And then um |
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| 28:15 | total field the Magnetics goes from, looks like whatever minus 30 minus Was |
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| 28:27 | , 40 56. This might be down here, but mostly and then |
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| 28:31 | to like overnight over 100. So use this. Alright, so gravity |
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| 28:41 | is the observed data on the left the band pass and there's some gray |
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| 28:47 | just edge effects. So that's that's that's being caused by artifacts produced by |
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| 28:54 | filtering. But um so this this is two and four kilometers. |
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| 29:01 | this is, it's anything Any anomaly shorter than two km and more more |
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| 29:08 | four km are cut up. So a lot of chatter, a lot |
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| 29:13 | jiggly lines in here. So that's these short wavelength one. So you |
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| 29:16 | to cut those out and then um wanted to I guess residual eyes see |
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| 29:24 | is only a two kilometer. So only like six kilometers across. So |
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| 29:29 | he's cutting, he's not really cutting away that much from this with |
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| 29:34 | four kilometer filter. Okay, here's observed uh with a different band pass |
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| 29:43 | this time it's 321, 300 to m to a kilometer. So now |
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| 29:52 | shorter than 300 m. So he's retaining a lot of the short short |
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| 29:57 | stuff, but anything less than one , anything less than that distance. |
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| 30:02 | so you see it's just totally flattened data, which is fine. If |
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| 30:06 | what you're looking for, you're looking really tease out all these little features |
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| 30:11 | here, then that's what you can . Bear in mind he's got |
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| 30:15 | you know, this this part that a little bit, you know, |
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| 30:21 | by the artifacts on the edge of map. Okay, this is just |
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| 30:27 | high cut. So it's just filtering everything that's shorter. I mean anything |
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| 30:33 | longer wavelength than one km and this completely flattens the data. I mean |
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| 30:38 | control is 1/10 of a mil gail like, you know, it's just |
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| 30:44 | flat. It's probably just noise, know, I mean, some of |
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| 30:48 | looks a little coherent. I wouldn't this thing. Maybe this is |
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| 30:53 | Maybe this is, I mean, definitely wouldn't trust this. Okay. |
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| 31:01 | Alright, so here's magnetic knowledge and is the same 2-4, so 2-4 |
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| 31:08 | this really changes the data a I mean it completely just, I |
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| 31:14 | there are a lot of anomalies that smaller than this, two kilometer range |
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| 31:19 | he's just whacking them, you there's a lot of little things in |
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| 31:22 | . He's just saying I don't need , so that's fine. I |
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| 31:26 | if that's what you want, if want to sort of, you |
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| 31:29 | see what the big components are in data, then that's, that's the |
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| 31:34 | you can do it here is a low pass. So everything that's shorter |
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| 31:45 | and four costs. This is a extreme low pass. I mean it's |
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| 31:49 | very smooth and just accept the longest component of these, these two anomalies |
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| 31:56 | And then here is a high cut of one km. So now he's |
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| 32:03 | a lot of these little features, can see all these little anomalies and |
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| 32:06 | is a trend that's measured in you can see that just pulling it |
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| 32:09 | out. So very effectively doing You know, this bullseye here, |
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| 32:14 | might be what whatever it is, some some intrusion there. Okay, |
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| 32:24 | a little case history in the Richmond , which is, which I think |
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| 32:31 | , it's up near um, whatever in the northeast in the Appalachian. |
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| 32:38 | , just, just outboard Appalachian, think somewhere. But no, it's |
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| 32:44 | Virginia, I guess the Richmond basin in Virginia, but it would be |
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| 32:52 | outboard of the Appalachians, it would in the coastal plain or the |
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| 32:58 | um east of the Appalachians and here the gravity stations all these plus |
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| 33:06 | They're everywhere, there's tons of And then there's the bouquet anomaly |
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| 33:10 | So are dominated. Okay, so observed they're dominated by this regional trend |
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| 33:16 | through you, right over the And so he says the basin boundaries |
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| 33:21 | line here that outlines the yellow shape exposed at the surface or expressed at |
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| 33:28 | surface. And then the gravity analogy Richmond basin is difficult to see because |
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| 33:34 | this just swamped by this gradient. the low associated with this basin is |
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| 33:42 | on the flank of a strongly developed . So you have this regional high |
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| 33:48 | the basin, you know the the minimum which would be which which you |
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| 33:53 | is over this basin. It's completely by the regional signal. Okay, |
|
| 34:02 | This is a 6th order order order trend. So you can see it |
|
| 34:09 | up with that when you subtract it the, from the original data, |
|
| 34:15 | does a good job. It starts pull it out but it has this |
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| 34:18 | like this and it doesn't really outline base and complete. Okay. Um |
|
| 34:28 | then um understood racism and this. he's okay. So this is a |
|
| 34:38 | fit. What is this one? a regional map of the, have |
|
| 34:42 | notes here. Hold on, I to make sure I get this |
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| 34:45 | This is three slide 33. So so the the. Okay yeah |
|
| 35:13 | is what happened the is that the map? It's not the same |
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| 35:19 | So this is this is a higher polynomial. Right? This is a |
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| 35:24 | order polynomial. I thought it oh oh I don't never mind. Never |
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| 35:45 | . Okay never mind. Okay. I think minimum curvature station. Oh |
|
| 35:59 | is what they did. This is he did right? He removed the |
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| 36:05 | uh outside of the basin boundaries. he went in here where the stations |
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| 36:11 | , he removed some of the stations then he did it then he did |
|
| 36:15 | trend surface. And so now lines with this sorry and now it lines |
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| 36:22 | with this better. And then he he subtracts it. He can he |
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| 36:27 | see the basin, right? He some stations to calculate the regional |
|
| 36:36 | Just doing the minimum curvature regional. basically basically he he took the stations |
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| 36:42 | and that way that made the regional just skim over the top of |
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| 36:45 | And then he subtracted the field from . So and then it isolates |
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| 36:52 | So he basically he basically did like hand contour of a regional through |
|
| 37:00 | Yeah basically a hand contour sort he just manually deleted the stations that |
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| 37:07 | contributing to it. And then he able to isolate the anomaly the the |
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| 37:13 | of the base. Um Yeah okay fine. That's fine to do you |
|
| 37:19 | I mean clearly this is one mg interval and clearly it's very deep |
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| 37:26 | It's a little broader. It's just . The shape is not accidental. |
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| 37:31 | the basin shaped like that. here's an example of strike building. |
|
| 37:37 | this is again, you know, a certain trend that you want to |
|
| 37:41 | . So um uh this is from others application of a band pass. |
|
| 37:49 | there's this called the steamboat fraser That's that's this trend? S steamboat |
|
| 37:56 | trend uh striking north south, north , a little bit. Right? |
|
| 38:07 | the strength. So the distance of 6, 600 km. So |
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| 38:11 | there's 400. So this is the is the this is gravity data. |
|
| 38:19 | is this is gravity jaden. Right miller Geils. And you have |
|
| 38:27 | trend in here that they want to . So they did this directional filter |
|
| 38:31 | 365 this way. And then. , I see a static and filtered |
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| 38:40 | , static gravity maps. A isIS gravity map, bees, directionally filtered |
|
| 38:46 | . Right? Oh, this is filtered data. Right? So we |
|
| 38:52 | here, is that Yeah. I see. This is this is |
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| 39:02 | trinity. What is you? I'm , I read this this morning and |
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| 39:07 | was, I thought I understood But now, but in any case |
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| 39:11 | just this is, you can see there's a dominant trend going through |
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| 39:18 | That I guess I think what I there's they want to enhance features that |
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| 39:23 | going in this direction. So they this direction Out. So they filter |
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| 39:32 | Anything going on that trend so that can enhance these anomalies that are in |
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| 39:37 | foothills because they're related to um in in the Canadian, the western Canadian |
|
| 39:47 | basin is right here. Okay, no two prominent seem about fraser |
|
| 40:11 | S. F. Right? And guess you don't really see that in |
|
| 40:18 | , is that right? That's what saying. Yeah. The dash line |
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| 40:22 | the right of sf south is the river fall fraser river fall. |
|
| 40:33 | Alright. Okay. Let's move Um The steamboat fraser. So here |
|
| 40:38 | a three D. Diagram. It may thus be interpreted either late or |
|
| 40:42 | or genic intrusive. That's what they're to say schematic interpretation. So here's |
|
| 40:48 | basement, here's the western Canada Um And here's that T. |
|
| 40:53 | N. R. M. That's this that's this feature right through |
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| 40:58 | and that is T. T. . N. T. Is a |
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| 41:01 | mountain T. T. Right? the tin tina trench and the |
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| 41:08 | N. T. S. Rocky traits. So that's that trend through |
|
| 41:14 | . So all right. So that's filtering uh in spatial domain and previous |
|
| 41:26 | continuation is actually filters as well. it's it's it's broadband. It has |
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| 41:34 | wavelengths in it. And what what doing is just say potential field theory |
|
| 41:40 | that the fields are one elevation, is no at all elevations provided. |
|
| 41:46 | there's no sources or sinks in other no, there's nothing that's going to |
|
| 41:55 | , you know, grab your magnetic over that over that difference in |
|
| 42:01 | So as you upward continue, which what most people do. They do |
|
| 42:05 | upper continuation to generate a regional So as you upward continue, shallow |
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| 42:12 | will change in amplitude more rapidly than deeper sources. Anomalies produced by deeper |
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| 42:18 | so they will disappear. And it's of the inverse distance relationship. Regional |
|
| 42:31 | can be generated by upward continuation. I said, then you subtract |
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| 42:37 | That's how you always do make a subtracted from the original data. Then |
|
| 42:42 | is a residual. You can do continuation as we saw with those uh |
|
| 42:50 | that mars example of potential yesterday last . But downward continuation can be perilous |
|
| 43:01 | you can't downward and continue into the because the algorithms tend to like blow |
|
| 43:07 | . They don't work very well. so down continuation is generally not |
|
| 43:15 | But I mean you could do it satellite data safe enough because you're just |
|
| 43:20 | coming as long as you don't down continue into the surface. Okay, |
|
| 43:26 | our friendly little baps again from bill , upper continued gravity knowledge. So |
|
| 43:32 | the left again is our observed gravity on the right is the upward continued |
|
| 43:37 | that. So upper continued 300 Then again you have the edge effect |
|
| 43:45 | . So same contour interval. So can see how all of a sudden |
|
| 43:50 | get smoother. All this chatter kind just attenuate and disappears. Now here's |
|
| 43:57 | downward continuation Downward continued 50 m. yeah, it produces edge effects all |
|
| 44:05 | way around. But you can see it's um it's it's really, you |
|
| 44:14 | , increasing the amplitudes of these, like the just like the deep toe |
|
| 44:21 | anomalies offshore Iberia versus the magnetic anomalies on the sea surface. It's like |
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| 44:29 | just getting close to the source so anomalies have to get bigger. That's |
|
| 44:35 | . And so here's Magnetics. So an upward continuation of 300 m. |
|
| 44:41 | country interval is the same for So you can see it really sort |
|
| 44:45 | as you say, flattens the It's sort of sort of smooths it |
|
| 44:50 | and reduces the amplitudes And the downward of 15 meetings. And and it's |
|
| 44:59 | it's really enhanced the amplitude of these . I mean it's almost scary. |
|
| 45:04 | can see it's kind of contour is little jiggly because you're you're really producing |
|
| 45:09 | content into it when you don't And this county was different. This |
|
| 45:14 | 10, this is 20, so , so they couldn't do this with |
|
| 45:19 | because it would be just like solid , you know, this is this |
|
| 45:22 | 10. Right? So yeah, , now derivatives basically, you know |
|
| 45:29 | rate of change horizontally or vertically? our way to enhance data. And |
|
| 45:38 | course we remember, we can just back to laplace equation right? Which |
|
| 45:42 | on here kind of written out more than just del squared. Right? |
|
| 45:48 | Del is Del is is the is X. The sum of the |
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| 45:53 | Y and z gradients? Right. this would be del squared times |
|
| 45:59 | G. Which is the gravity uh potential ins okay, so the maximum |
|
| 46:12 | minimum will tend to lie directly over cause of the body for a uh |
|
| 46:19 | vertical derivative. Right? That's where , that's here. And if you |
|
| 46:27 | a horizontal relative in X. So you're going from left to right, |
|
| 46:34 | light blue. So in other as you approach this anomaly it's |
|
| 46:37 | increasing in value. But then when hit the inflection point, it flattens |
|
| 46:42 | and starts decreasing, decreasing in amplitude X. So that's why it turns |
|
| 46:48 | here and it's back to zero in of rate of change, that's why |
|
| 46:52 | crosses here. And then it's uh then it is uh from zero and |
|
| 47:00 | it continues to decrease in decreasing, until that's the inflection point here. |
|
| 47:06 | and that's that's what decreasing with respect acts that it starts increasing and increasing |
|
| 47:10 | increasing until it comes back to So that's why this shape, this |
|
| 47:14 | is directional, You have to be be pay attention to that. |
|
| 47:20 | that's why people have to do second derivative. Because you do you make |
|
| 47:26 | you take the derivative with respect of field with respect to X. Going |
|
| 47:32 | left to right and you do it right to left and you just end |
|
| 47:36 | with this symmetric announcement. Okay, that's the relationship between political derivative and |
|
| 47:45 | derivative are typically directional. Okay. the the standard for that is is |
|
| 47:57 | N E D. Northeast down. north means going from south to north |
|
| 48:05 | and he's going from west to east down. Well, we're not doing |
|
| 48:09 | vertical that way, but it's any , I guess. So, here's |
|
| 48:15 | another picture sort of of the same . Uh This is from Heinz |
|
| 48:19 | So um if you have some tooty cylinder that's passing in and out of |
|
| 48:24 | plane here, that produces the And out of the solid line it |
|
| 48:30 | um What is this first vertical F B D. That's this desk |
|
| 48:36 | here, so it's symmetric and then produces first horizontal F H E |
|
| 48:41 | That's just dot in line what we before it increasing, increasing, |
|
| 48:46 | It's the reflection point starts decreasing to peak where it's zero but it's still |
|
| 48:53 | , going down to here to the point and back up and then the |
|
| 49:00 | vertical. So this, so this the first vertical derivative and then the |
|
| 49:04 | vertical differences dot dash here. So derivatives? You can think of those |
|
| 49:11 | sort of tightening the anomaly. The gravity anomalies here. First derivative |
|
| 49:17 | it to here. Second vertical derivative it even more to hear. |
|
| 49:24 | you can kind of think of making it sharper and sharper, trying |
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| 49:28 | approach like a impulse almost directly over source. Now, horizontal initiatives are |
|
| 49:41 | called edge detectors, right? And do the same thing with vertical drills |
|
| 49:46 | , don't they? That's a pretty because here this is the first |
|
| 49:56 | the first horizontal drift is showing this , right second. So yeah, |
|
| 50:02 | horizontal derivatives are called edge detectors because see it's producing a high over the |
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| 50:08 | the edge with the inflection point of source body. This horizontal sheet ends |
|
| 50:14 | so that's where it's going to produce anomaly. So the first horse, |
|
| 50:18 | don't do evidence, is there the field, is this right here just |
|
| 50:22 | that and the inflection point is right the edge. And then the vertical |
|
| 50:29 | basically tighten this shape. As I , the first vertical derivative looks quite |
|
| 50:35 | to that. The second one. , you see the inflection point is |
|
| 50:39 | same. It's just that this shape is kind of continually changing but it's |
|
| 50:47 | like going from low to high. , I hope you can see that |
|
| 50:51 | real quick on the previous slide for S. V. D. And |
|
| 50:59 | F. V. D. Is the same thing as like a Richter |
|
| 51:02 | lit because I know the shape is right? You can view the right |
|
| 51:07 | like a minimum phase you mean? . Yeah. Right. But but |
|
| 51:13 | about it this way you're taking the of this is the anomaly. And |
|
| 51:17 | taking the rate of change with respect X. With respect to Z. |
|
| 51:23 | this case respect to Z squared for one it's the rate of change with |
|
| 51:27 | to X. And respect to Again and then with respect to |
|
| 51:34 | Okay so in this one it was . And second. So this is |
|
| 51:41 | and horizontal. To and on these vertical and the second vertical. So |
|
| 51:51 | and so that's I think that's why put them in like that. And |
|
| 51:56 | this one which is a little bit difficult because it's like you're on the |
|
| 52:00 | . But so the gravity field does . But now this is this is |
|
| 52:04 | interesting part. You see that and come across this again where this the |
|
| 52:11 | derivative of an edge looks like the over over even a vertical source or |
|
| 52:25 | point source. And that's an interesting that is exploited by some some programs |
|
| 52:34 | depth estimation. So yeah but so gravity field does this that means the |
|
| 52:42 | . Remember I said sort of tightens anomaly. But you can think of |
|
| 52:45 | as just being one side of It's sort of enhancing that, that |
|
| 52:51 | of this edge enhancing the shape. the edge lies still in the inflection |
|
| 52:57 | the inflection point. So I don't if I'm answering your question but |
|
| 53:06 | Okay. Yeah, because I mean is sort of like, right, |
|
| 53:09 | this sort of like a minimum face of feature sort of directly over the |
|
| 53:15 | that the idea of a minimum Alright, okay. So probably Utah |
|
| 53:24 | knows. So. Okay, so right, so here's our here's some |
|
| 53:30 | map. So here's our observed There's our first vertical derivative. So |
|
| 53:35 | intervals from 10 to the 10th of manifesto per meter. Well, this |
|
| 53:42 | like a high pass filter and in four A. Domain. These formulas |
|
| 53:48 | very similar. I think. I they can be transposed from one to |
|
| 53:53 | from a derivative to a band. think there's a way to do |
|
| 53:57 | And then here's this the second vertical and it's completely flat. I |
|
| 54:03 | I don't know if there's any Yeah, that's just a zero |
|
| 54:13 | Okay, so here is, I'm drawing a lot from Nettleton but out |
|
| 54:19 | respect. So here is um Where this? I just 47. Um |
|
| 54:30 | see this is in Los Angeles basin it's a very steep gradient was contoured |
|
| 54:38 | point to Milligan's. So this is , 35, 40, 45 50 |
|
| 54:48 | sort of going from 10 to $60 . That's a pretty steep 50 million |
|
| 54:54 | . What's the distance? That's one over looks like maybe seven or eight |
|
| 54:58 | . So that's a pretty steep And then um so this is the |
|
| 55:05 | vertical derivative of this and you get . And so and and these um |
|
| 55:11 | shade 100 base covers. They calculated The shades are I guess those fields |
|
| 55:27 | say it here but I think they like you're trying to enhance these features |
|
| 55:33 | it's not really lining up but actually is not bad, not bad. |
|
| 55:38 | Yeah okay derivatives. So analytic signal that we can take these directional derivatives |
|
| 55:49 | . Y. And Z. And Z if you like. And |
|
| 55:51 | can combine those in what's called the signal and it's basically the root of |
|
| 55:58 | sum of the squares of each directional . So you can think it was |
|
| 56:03 | a like a distance formula And absolute is called the energy envelopes. Single |
|
| 56:13 | the root of the sum of all directional drill. And it displays a |
|
| 56:19 | where the density magnetization changes abruptly. it is also useful as an edge |
|
| 56:28 | for Magnetics to maximum locations independent of the field direction and the magnetization. |
|
| 56:35 | the thing about analytic signal for That's handy is that you no longer |
|
| 56:41 | to worry about inclination, declination. It will, it accounts for that |
|
| 56:46 | it's just taking the rate of change that the value. Uh It does |
|
| 56:55 | with field directions but the shape, shape of the analogies. So it |
|
| 57:01 | be used to estimate source steps. here's a comparison shape. Ple signal |
|
| 57:09 | the same regardless of the orientation of magnetic field. So the magnetic field |
|
| 57:13 | . A. Signal. So here um So here's the horizontal relative at |
|
| 57:21 | inclination. Here's pseudo gravity. For reason it's in here. Let's just |
|
| 57:26 | that. But so here's so okay here's the magnetic field at the bottom |
|
| 57:31 | the source. This source party where field is inclined following this arrow versus |
|
| 57:39 | source. The source where the field vertical. Like at a poll this |
|
| 57:43 | be 45 degrees in the northern Because you see, let's just say |
|
| 57:48 | going south is to the left and is to the right. So that |
|
| 57:53 | but the fuel is inclined this way have a high low pair the highest |
|
| 57:58 | the south and the north end is the it's a minima at the northern |
|
| 58:06 | . You see that remember we talked that shape. So but and then |
|
| 58:14 | horizontal derivative of this looks like So now at a poll this thing |
|
| 58:21 | produce a nominee like this. And horizontal derivative would just be, it's |
|
| 58:27 | find these edges right But the analytic of the same source body is the |
|
| 58:37 | regardless of either one of those. that's what's handy about Emily signal I |
|
| 58:43 | I have a 3D. Sort of let's see here. So so here's |
|
| 58:49 | we start with this three D. and then we calculate the magnetic |
|
| 58:54 | So in this case it's in the hemisphere you have a high over the |
|
| 59:00 | corner, a southern edge and a over the northern edge. If you |
|
| 59:04 | see that the horizontal derivative looks like the horizontal horizontal X. Looks like |
|
| 59:12 | horizontal. Why it looks like The vertical derivative looks like this so |
|
| 59:18 | just squeezing these enough just like So it's gonna really narrow this. |
|
| 59:24 | as a high and narrow this is low when you calculate when you combine |
|
| 59:28 | and calculate the signal, it looks this. And then you can take |
|
| 59:37 | the uh the uh what you do you calculate the distance from inflection to |
|
| 59:46 | where it's where the gradient where the gradient is zero across here. And |
|
| 59:54 | distance is related to the source So this these solutions should lie along |
|
| 60:02 | top of this source. So that sense. You follow that. Yes |
|
| 60:13 | . So they use this stuff. Here's the paper by, well this |
|
| 60:17 | is continuation is walter roast. Um so they made the synthetic model And |
|
| 60:24 | they produced the future with the inclination Declination 20. Um And then they |
|
| 60:36 | Alex signal. And then they just uh uh did the did the calculations |
|
| 60:44 | the source steps are followed these these so 345 and six, I'm guessing |
|
| 60:53 | . Source steps and they work pretty . Things always work good on synthetic |
|
| 61:03 | . Okay, so total horizontal gradient also an edge detector as it as |
|
| 61:10 | should be. Its X and Y signal is an edge detector but it's |
|
| 61:15 | kind of a kind of a uh orientation annihilated, right? But if |
|
| 61:24 | just drop the the D. Component, directional derivative and just use |
|
| 61:32 | the X. And Y directional we can you still enhance the |
|
| 61:36 | right? Because it's still X and . So this is a study. |
|
| 61:42 | let's see soon at all. This is yeah. So they say |
|
| 61:50 | have developed the spectral moment method for edges to protect a few analyses based |
|
| 61:55 | the seconds actual moment and it's statistically in variable quantities. E.G.M. |
|
| 62:03 | so they're using a using a global model to do this with and um |
|
| 62:11 | propose a new method. So they on the upper left are gravity anomalies |
|
| 62:20 | the upper right are photo total horizontal and then profile curvature development cooper Collins |
|
| 62:29 | suggest improvements in the normalized standard And then they're proposing a liniment |
|
| 62:43 | Yeah. Yeah. I make a to myself, These guys are a |
|
| 62:51 | of geophysicists in other words they're not not really geologically inclined because I |
|
| 62:57 | I guess this all makes sense, not sure. But you can I |
|
| 63:05 | like the literature is just this, know, the the number of papers |
|
| 63:11 | like, especially journalists like geophysics where are combining and coming up a new |
|
| 63:17 | to view the data, it's just just lots of it and it's not |
|
| 63:24 | bad thing. I'm not I'm not critical. I'm just saying there's a |
|
| 63:26 | of different ways that you can combine things and this is one way that |
|
| 63:31 | they're trying to extract some geologic Um but but the thing is that |
|
| 63:37 | not really okay, is there any . Okay, let me go back |
|
| 63:41 | . So, so this is, here's your profile and this is liniment |
|
| 63:48 | . What's the next one here? here's still the top to the |
|
| 63:53 | But now they're doing this tilt So tilt angle is a derivative method |
|
| 63:59 | the the angle is the co sign um the total horizontal gradient I |
|
| 64:11 | And there's and then you can normalization the horizontal, great with the |
|
| 64:16 | So now they're they're combining horizontal, with led signal and that's what they're |
|
| 64:21 | up with down here, there's another , right? Okay, totally, |
|
| 64:25 | same. Top same. But now trying to normalize standard deviation based on |
|
| 64:30 | derivative and this kind of this wormy . And then finally, the edge |
|
| 64:35 | , new method proposals based on spectrum which which is supposed to handle noise |
|
| 64:40 | . So yeah, I mean, know, they're just they're just having |
|
| 64:46 | of fun figuring out ways to enhance data. What bothers me about this |
|
| 64:51 | is there's no geology in it. mean they do show this, they |
|
| 64:56 | this map and I think this is study area here and the box down |
|
| 65:01 | , but then they just start doing these calculations. All right. So |
|
| 65:11 | mentioned the charter and this is it that the tilt angle is the inverse |
|
| 65:18 | of DZ over the horizontal gradient. . And if you look at this |
|
| 65:26 | here, so this the horse upgraded is this and then the vertical gradient |
|
| 65:40 | this bit. So this angle that's tilting. Okay. And note that |
|
| 65:51 | quantity is actually also D. In this formula down here. So |
|
| 65:58 | can use a tree limb metric identity reduce this to alpha page. I |
|
| 66:07 | theta over D. H. Is to that and death Z here is |
|
| 66:15 | over D. H. H. . Yeah, this is this is |
|
| 66:34 | confusing to me a little bit. um because you see they come down |
|
| 66:41 | in their throat. Yeah, the this yeah, goes to here traded |
|
| 66:53 | H. Okay. Yeah, I that's how it works. Um I'm |
|
| 67:08 | to figure it out though, but don't really remember um 56 6. |
|
| 67:18 | right, I can come back to but Oh it's -1 over Okay never |
|
| 67:29 | . But but any case the the is the inverse tangent of the vertical |
|
| 67:37 | the horizontal gradient. And of course tangent is defined between minus minus pi |
|
| 67:43 | two and pi over two. Use identity. And they calculate the depth |
|
| 67:49 | this thing. I'm not really I that this is related cause I put |
|
| 67:52 | in here but it's not clear how related. I know it's related in |
|
| 67:56 | case Z. Is this quantity? they estimate death from it? So |
|
| 68:06 | the A. Is the the total R. T. P. The |
|
| 68:11 | field here's the tilt angle so it's radiance. So minus you know this |
|
| 68:19 | minus pi over 22 pi over And then um total horizontal gradient of |
|
| 68:26 | tilt angle and then the total horizontal of that is the vertical gradient of |
|
| 68:32 | tilt angle. And here's the depth that equation what you see and then |
|
| 68:39 | some so they also calculate a structural which is supposedly related to what kind |
|
| 68:45 | a structure is if it's a point or if it's a it's an edge |
|
| 68:49 | something like that. You don't need worry about all this stuff. You |
|
| 68:55 | you need to know that they use and to uh to estimate source steps |
|
| 69:01 | here's what's interesting These sources they're just these they're just following these gradient |
|
| 69:08 | right? I mean, you evidently the depth ranges from 0-3, |
|
| 69:13 | they're all mixed together in here. mean, I don't know how you |
|
| 69:17 | , I mean, I'm not crazy death estimation techniques that do it this |
|
| 69:22 | in maps, because I mean, always seem to like file up different |
|
| 69:27 | estimates on top of each other and want to really look at it through |
|
| 69:32 | section to figure out what these are . And in any case it's just |
|
| 69:37 | the gradients. I mean, here's total field, I can just I |
|
| 69:40 | just draw a line through that. , I don't really need to do |
|
| 69:42 | this to tell you where the gradients . So um I think that there's |
|
| 69:49 | geophysics than geology going on with some these enhancements and um yeah, so |
|
| 69:58 | you have it, that's what I . Okay, now wavelet transforms, |
|
| 70:02 | I said, they were pretty like in the late 90s and early |
|
| 70:07 | Ridsdale Smith is the guy, he did a lot of the early worked |
|
| 70:14 | it, They have several uses source interpretations, death destinations. There are |
|
| 70:22 | 2 types. There's continuous continuous wavelet and discrete wavelet transforms. Um um |
|
| 70:34 | see in some respects, some of windowed fourier transform, however, instead |
|
| 70:40 | decomposing the signal in assigning co sign signal is decomposed using wavelet functions of |
|
| 70:48 | length. So basically you just you basically involving against different wavelengths. Uh |
|
| 70:56 | yeah, so the scale is in of different wavelengths that they use, |
|
| 70:59 | what does that look like? So other words you have you have some |
|
| 71:05 | wave like some other wavelength and you squish it or extend it. So |
|
| 71:11 | it's you know or dilate it, it take it and then of course |
|
| 71:15 | can and then you just move this lit and you basically combine it with |
|
| 71:22 | data. Kind of a convolution. for example you have you have this |
|
| 71:29 | of just continuous wave it where you to start up at the top and |
|
| 71:34 | gets and you dilate it going down the bottom and you move along and |
|
| 71:41 | I guess combining I guess you integrate quote involvement with your data. And |
|
| 71:47 | depending on your day looks like you're have some sort of feature like this |
|
| 71:52 | it's working here. The data is with this shape here but inconsistent. |
|
| 72:00 | then it flips to this way But you can do it discreetly which |
|
| 72:07 | more effectively because you don't have to it so much and you can just |
|
| 72:13 | know do it in powers of two down to the table. So so |
|
| 72:21 | is kind of what that would look and then this is a little complicated |
|
| 72:24 | the magnet. So here's your here's your data and is the fast is |
|
| 72:30 | wavelet transform of the magnetic profile generated two D. So this is the |
|
| 72:36 | D. Sources, Right. And this is the profile and then so |
|
| 72:41 | wavelet transform, you're looking at the of the transform as shown by the |
|
| 72:48 | lines with the length proportional to the . And their plot is a functional |
|
| 72:55 | scale. So you wave it you different passes and and the coefficients are |
|
| 73:03 | in magnitude by the length of these lines and that's how it keeps track |
|
| 73:07 | it. Yeah. Okay. All that's enough of that. Does that |
|
| 73:14 | sense to you? I mean it's it's just another way you're basically you're |
|
| 73:19 | the wavelength and you're basically keeping track the values of how it how it |
|
| 73:26 | the data. And then you can basically have these digital sources for various |
|
| 73:34 | scale wavelengths that when you combine all you can return back to this that's |
|
| 73:39 | idea. But this allows you to ahead. No I was saying because |
|
| 73:47 | thought you asked if I was doing . Yes I'm fine. Okay |
|
| 73:51 | Okay so now um R. P. Reduced the magnetic pole. |
|
| 73:58 | It's an operation and it's based on assuming you're assuming that all the source |
|
| 74:07 | the sources that are producing families are by the inducing field. Okay so |
|
| 74:16 | have this formula where's inclination, C sign, declination, inclination inclination. |
|
| 74:28 | problem is yeah I and er That the problem is with this is |
|
| 74:35 | um hair your your scaling by So what happens when this term goes |
|
| 74:45 | zero? That means the thing doesn't . So that's one problem. The |
|
| 74:53 | problem is that all rocks have all rocks. As I pointed out the |
|
| 74:57 | day all rocks have a really high a racial as I showed in those |
|
| 75:03 | last night. And so that means there are high levels of remnants. |
|
| 75:09 | the assumption that is just inducing field working. I mean is the only |
|
| 75:16 | that magnetized the rocks in the area absolutely incorrect. And then okay if |
|
| 75:25 | doesn't work at the magnetic equator if starts distorting the data and it does |
|
| 75:31 | starts pulling the data like elongating anomalies the north south way. It's once |
|
| 75:38 | get down to like 20 degrees. if it's creating artifacts in the data |
|
| 75:42 | you see at 20 degrees it's creating everywhere else. You just don't see |
|
| 75:47 | . They're just not knows now people know people like this stuff they say |
|
| 75:54 | but I can tell it's working because looks like it's right you know I |
|
| 76:00 | know if I'm buying into that but so R. T. P. |
|
| 76:06 | mean it's it's here I mean people there are lots of people changing their |
|
| 76:11 | about it but it's just gonna be . You know people just have to |
|
| 76:16 | be around anymore. Okay, so is one thing we do and here |
|
| 76:21 | I mean we we went through this of but this is sort of the |
|
| 76:23 | thing we looked at yesterday. So same source body will produce different shaped |
|
| 76:28 | depending on where it is. So symmetrically positive. Again, equator |
|
| 76:35 | northern hemisphere high low pair with the of the south, don't have the |
|
| 76:40 | of the north. So everything we about the issue here is the south |
|
| 76:43 | , Here's the north pole, here's equator, right? So these are |
|
| 76:47 | going south to north. They all . No, they're not there. |
|
| 76:53 | at how horrible this is. These going south to north. This has |
|
| 77:00 | north to south. Yeah, north south. Look at that south to |
|
| 77:07 | south and north north to south A prime. Yeah. So they |
|
| 77:10 | . You shouldn't do that, but can see the same thing. So |
|
| 77:14 | lo pare the highest of the south eyes to the north. And then |
|
| 77:21 | little thing here. So yeah, is all we showed yesterday. All |
|
| 77:27 | , So here's our little uh hinds again. And this is our |
|
| 77:32 | P. Mag with a filter cut uh contour null. And it's it's |
|
| 77:38 | and so here's the high low So this is a very high latitude |
|
| 77:44 | degrees. Here's the high low this is shifted north and is covering |
|
| 77:50 | this one this phenomenon is shifted north it's not as noticeable I guess. |
|
| 77:55 | yeah there's the high, here's the so this here's the high, here's |
|
| 77:59 | low that's shifted north. All Uh differential R. T. |
|
| 78:09 | So differential R. T. Is a new thing. But the |
|
| 78:14 | is that it's saying it's still assuming is induced and still something that there's |
|
| 78:18 | remnants and you know, I think manages. I mean there are ways |
|
| 78:23 | calculate our teepee at the equator but have to do it with equivalent source |
|
| 78:30 | and it's fairly complicated. Okay so when we do an R. |
|
| 78:41 | P. The classical I say the old school is you just pick at |
|
| 78:47 | point in the center of your survey you say and you find the |
|
| 78:52 | G. F. Uh the R. F. Parameters, you |
|
| 78:59 | , inclination, declination, field strength that location. And you just you |
|
| 79:07 | you transform everything to us using those values. But in 88 they figured |
|
| 79:17 | a way to to do the RTP shift for every single data point. |
|
| 79:23 | here's our data. So this is the southern hemisphere cooper cooper's Australian. |
|
| 79:29 | it's got to be southern hemisphere. the highest north of the body. |
|
| 79:33 | ? And this is this is for what this is for. So it's |
|
| 79:39 | for three different sources but their inclination different, right? The mechanization is |
|
| 79:46 | little bit different any case. So a standard R. T. |
|
| 79:49 | So it still has a little ghost a low there. And this one |
|
| 79:55 | be completely resolved very well. And one is still got a little bit |
|
| 80:00 | a it's stretched a little bit but is the differential one. So here |
|
| 80:05 | all three about the same. So can see that our teepee standard didn't |
|
| 80:11 | do as good a job as um . And everyone's doing differential now. |
|
| 80:19 | it's still like you know it's I'm not crazy about it. Okay so |
|
| 80:27 | a study of reduced the pool in differential. RTP in Australia. Where |
|
| 80:36 | this at? This is This is pages. This is 67. |
|
| 80:43 | So this is This is Cooper and 2005. This is the northern |
|
| 80:49 | So this is the the whatever. think this is just showing the basement |
|
| 80:53 | marquee and you have some origin. rocks and uh the colors didn't come |
|
| 80:59 | in these. This is the magnetic and Australia. I mean north |
|
| 81:06 | You can get magnetic data for all north America. That's open filing. |
|
| 81:10 | good. You can get it for Southeast Asia prefer you get for Australia |
|
| 81:19 | uh in Russia. But you can't it very good for any place |
|
| 81:24 | There's a mag too which is kind crappy data. Not very good |
|
| 81:29 | Um But this is obviously very beautiful . I mean look at the |
|
| 81:34 | I mean this this is the Amadeus . Yeah, this is the Amadeus |
|
| 81:39 | here and then um This is the can't read that. But this is |
|
| 81:48 | basin here through here. So you see that here's how you know it's |
|
| 81:56 | basin. It's just long wavelength, smooth. And this is this is |
|
| 82:02 | is outcropping. This is is It rocks here. It's very short |
|
| 82:07 | , very chattering. So it's just here. The basement is deeper. |
|
| 82:12 | that's that's how you can tell. very smooth areas. This is a |
|
| 82:17 | up here. Right. Yeah. is a basin. This is this |
|
| 82:21 | here. McArthur basin I guess. call And I know that because I |
|
| 82:27 | look at the wavelengths and see it's with this is a preview but I |
|
| 82:33 | looking at gravity anomalies um They're very structural highs will produce anomaly highs for |
|
| 82:43 | most part except for very long Words with magnetic data, you can't |
|
| 82:48 | at it that way. Um It's to magnetic data in terms of wavelength |
|
| 82:55 | wavelengths like all geophysics long wavelengths are by deep sources and short wavelengths are |
|
| 83:03 | by shallow sources. So with that mind you look at this map and |
|
| 83:06 | around and say, okay, it's long way. There must be, |
|
| 83:09 | must be deep here and all of sudden it's getting shallow early. This |
|
| 83:12 | be all shallow. So even though is blue, you know, suggesting |
|
| 83:18 | suggesting that it's it's leslie less magnetization this. It's still shall all these |
|
| 83:25 | wavelengths on top of it. So that's a pitfall. People look at |
|
| 83:32 | magnetic data, they try to see highs and lows as being structurally high |
|
| 83:36 | low. And that's you shouldn't do . You should just worry about |
|
| 83:41 | Do you see that? Mhm. you know that before? No, |
|
| 83:50 | assumed that it was structure. So glad you said it that way. |
|
| 83:55 | . Yeah. That's I mean that's that that is something that you come |
|
| 84:01 | a lot of folks who look at that way. And you know, |
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| 84:05 | and and a map like this really to kind of disabuse them of that |
|
| 84:10 | because and then over here, you , they're like they're intermediate wavelengths, |
|
| 84:14 | ? So they're there is maybe some over here. But just if you |
|
| 84:18 | use your eye to sort of look this in terms of wavelength instead of |
|
| 84:23 | . All of a sudden you can where it's shallow and deep. And |
|
| 84:26 | why when I look at a map this because see this this stuff |
|
| 84:30 | these are intermediate. These are under these are buried. But this |
|
| 84:36 | So the bases deep here. But coming up but it's not. But |
|
| 84:40 | this part here that's just at the . So, okay, so now |
|
| 84:46 | the on the uh C. This is total field and this is |
|
| 84:53 | teepee but we're in the southern hemisphere so things have to migrate to the |
|
| 84:58 | . So this one goes to the right here to boom right this guy |
|
| 85:04 | what's going on? It's migrating Everything's migrating southward because we're in the |
|
| 85:09 | hemisphere. So here's traditional RTP and differential R. T. P. |
|
| 85:16 | then I think they zoom in on box here. Do I have it |
|
| 85:19 | ? Yeah. Yeah. So they're that pseudo they did a pseudo |
|
| 85:24 | Remember I said gravity pseudo gravity. you assume that that that the magnetization |
|
| 85:33 | to produce a magnetic anomaly You can . So it's a way of saying |
|
| 85:37 | these these are the densities of the that were magnetic. But I mean |
|
| 85:46 | I think you can really get into trouble doing that. Um Okay so |
|
| 85:54 | the even the advanced difference Susan Rock was dominated by the dominated by the |
|
| 86:01 | field and then it cannot possibly be Collins burger ratios of ocean basement rocks |
|
| 86:07 | strongly biased. I not most kind rocks are biased as well. Now |
|
| 86:15 | a really nice favorite by this guy where he explains 11 methods for estimating |
|
| 86:21 | from magnetic data classic paper. Um yeah I think there's I mean it's |
|
| 86:29 | that people are looking at they're also at magnetic vector inversion which is kind |
|
| 86:34 | new stuff. I don't even talk it. Yeah, I gotta add |
|
| 86:37 | to my stuff but M. I. Is a thing. And |
|
| 86:41 | that tries to it tries to estimate total field vector by inverting against amplitudes |
|
| 86:53 | then it combines those amplitudes to figure to suggest what the what the magnetization |
|
| 86:59 | of the rocks are over a mapped . So if you know that then |
|
| 87:07 | you know the inducing field then you , you know, you scale the |
|
| 87:10 | field to be on the same scale the map vector. And then you |
|
| 87:17 | figure out remnants because the sum of plus inducing deuce vector. That vector |
|
| 87:24 | is equal to the total magnetization So that's the kind of stuff, |
|
| 87:28 | one of the methods clark talks about his paper. But yeah, that's |
|
| 87:35 | smarter ways to do it. And lot of folks, you know, |
|
| 87:38 | say, well it's too complicated you know, for management and I'm |
|
| 87:43 | , I mean, I mean, many different seismic attributes are there? |
|
| 87:47 | like 1000 of them, aren't And yet what are you actually |
|
| 87:52 | You are measuring with size that you're frequency amplitude and phase yet you |
|
| 88:00 | you know, whatever. A couple doesn't attributes. So I mean, |
|
| 88:04 | on, people can learn about, know, magnetic vector, they can |
|
| 88:09 | remnants and stuff. That's my Okay. Yeah, so here's pseudo |
|
| 88:19 | . I know I've been kind of explaining it but but the gravity field |
|
| 88:24 | from magnetic field measurements by means of relations. This is what I mentioned |
|
| 88:30 | and the calculation of conversion of susceptibility density and vertical integration of reduced to |
|
| 88:36 | . See you're already starting off. so poisons relation consider a source of |
|
| 88:43 | density. Then you make use of between gravity potential magnetic potential field |
|
| 88:52 | And V. Respectively. Right? this is the potential for gravity's potential |
|
| 88:56 | Magnetics. And you equate like terms you come up with this, this |
|
| 89:02 | that magnetic potential is proportional to the of gravity in the direction of |
|
| 89:09 | Fair enough. And uh yeah that's idea. You're basically saying you can |
|
| 89:15 | magnetization with density, that's the idea you can do a mathematically no |
|
| 89:24 | So of course I have to have study. Um um I'm just ripping |
|
| 89:33 | that stuff. I guess this is . This is the end of |
|
| 89:37 | I think man alive, I'm going too fast to this material. People |
|
| 89:47 | me more questions. I think you ask any questions. It's not |
|
| 89:50 | Um I'm like I'm one of those were like when you're telling it to |
|
| 89:55 | it's making sense. So I always to go back and review everything and |
|
| 89:58 | like okay like this is weird. that I should have questions next |
|
| 90:04 | So you have questions. You don't questions from last night then? |
|
| 90:08 | I I liked yesterday's it was it good yesterday. So I just need |
|
| 90:12 | review everything from today and but I so far so good. Like nothing's |
|
| 90:19 | confusing to me yet. So I I'm okay. It's not difficult, |
|
| 90:26 | . Yeah. I mean it can made difficult but there's no reason to |
|
| 90:30 | it. So. Alright, so this this wilkes subglacial basin in eastern |
|
| 90:40 | . Okay. And east antarctic ice interpretation of aero magnetic data flown and |
|
| 90:48 | yellow outline survey suggest a broad back basin and it's bounded by fold and |
|
| 90:58 | belt. The depth, it's a ranges from 1.5 to 3 columns and |
|
| 91:05 | already three columns of ice. So here's the arrow magnetic data and here's |
|
| 91:15 | little rift basins in here that they're . But I mean you know, |
|
| 91:22 | really skeptical of that because what's the -100. So these are hundreds of |
|
| 91:32 | of embassy. I think that that's kind of nervous about that because I |
|
| 91:38 | think you can I mean it's so . I mean it's such a high |
|
| 91:44 | anyways. So what they did was So let's see the top is |
|
| 91:51 | Oh I'm sorry down here. What's answer to 252. It's still pretty |
|
| 91:57 | up. This is the magnet that's . So the Magnetics does just correlate |
|
| 92:04 | . I guess these features. What did they do? So |
|
| 92:15 | so, So here's upper continued that's a top 10 km upper |
|
| 92:24 | So it's a reasonable I needed the grain of pseudo gravity. All |
|
| 92:32 | Yeah, I am having a hard making sense. And then here's the |
|
| 92:38 | of the arrow Magnetics. You just just this just smells like Is |
|
| 92:46 | one more? Okay, here's the . So they needed three D oil |
|
| 92:51 | and see that the deaf solutions are coded uh, in kilometers but they're |
|
| 93:00 | piled up. So you have like on top of greens. So which |
|
| 93:04 | one do you use, you know your stations? Bottom left, two |
|
| 93:14 | water solutions. So these are water . I'm more comfortable with that. |
|
| 93:20 | water solutions, all depth estimations. work on wavelength even tilt, but |
|
| 93:27 | works on weight and um longer deeper sources. Short wavelength shall forces |
|
| 93:35 | course. And um basically Warner. like you would say like you would |
|
| 93:47 | the profile, you know, in dozen places or whatever. And from |
|
| 93:54 | you you have water assumes a thin source and and so you so you |
|
| 94:06 | the jam to the source and you you pass a bunch of windows that |
|
| 94:11 | the profile at different different, you distances. So the longer distances will |
|
| 94:17 | better. So how it works is if you have a window that doesn't |
|
| 94:24 | sample the field very well. It calculate solutions very well, so it |
|
| 94:28 | even post them yet and but if captures this anomaly, for example very |
|
| 94:35 | and then we'll start posting solutions so set it up to, you |
|
| 94:39 | like to, you know, to when it will solve based on, |
|
| 94:44 | know, uh how well a goodness fit and then that's how it |
|
| 94:50 | So this longer wavelength is gonna put deeper sources, This shorter wavelength improve |
|
| 94:55 | sources and this little chatter is going produce really tiny sources. Right? |
|
| 95:00 | super shallow ones. What I can you look at this, all these |
|
| 95:06 | do is they follow the gradients but it is deeper. What is this |
|
| 95:10 | three km down here. So I'm looking at this and my my nickel |
|
| 95:15 | is that, you know, is you have good solutions here and here |
|
| 95:20 | this maybe not there, but that this is kind of hard to |
|
| 95:25 | but it's probably coming up on the of the basin, you see what |
|
| 95:28 | pointing at. So these solutions, wouldn't even think about these because they're |
|
| 95:36 | isolated and then the surface ones, , you know, their surface |
|
| 95:40 | I mean at the top of the topography is top of the Yeah, |
|
| 95:46 | basically your confidence is a function of well these guys cluster because what's happening |
|
| 95:52 | that they're satisfying their, you their their fitting the data really well |
|
| 95:58 | that whatever wavelengths are being passed through . So looking at so here's their |
|
| 96:05 | cross section and they've got solutions but you can see what they're looking |
|
| 96:10 | their solutions here. Um Yeah, looking at these solutions here. I |
|
| 96:17 | that's the only way I can I'm assuming this profile goes along through |
|
| 96:22 | , but um yeah, I don't . I mean, I have no |
|
| 96:29 | what you're going to get out of pseudo gravity. I know people do |
|
| 96:32 | , I know that there's people that products based on pseudo gravity, but |
|
| 96:36 | think it's I don't know. I it's kind of flawed. That's just |
|
| 96:39 | idea, my point. Um And here, I guess here's your final |
|
| 96:47 | map on the on the magnetic So it looks very much similar to |
|
| 96:53 | you started. And then, so they think it's a back arc |
|
| 96:59 | Um How Big Is It? It's . Where's the subduction zone over |
|
| 97:10 | Yeah, I don't know. That's far. This is the transit. |
|
| 97:17 | Sure. Yeah, I think I it anyways. Um Yeah, I |
|
| 97:26 | that. Okay, wait, I have I have a All right. |
|
| 97:30 | want to take a break. I have a lot more. I think |
|
| 97:33 | slide are we on this is, , I have a bunch more to |
|
| 97:36 | so we can take a break. you want to Yeah, we can |
|
| 97:40 | a quick one. Let's take about if you don't mind. Okay no |
|
| 97:48 | . Are you ready? Yep. . All right so um I'm gonna |
|
| 97:56 | gonna look at now um I'm gonna you a bunch of anomaly enhancements over |
|
| 98:03 | gulf of Mexico basically the gulf coast the northern gulf of Mexico and gulf |
|
| 98:09 | . So we've been talking a lot all these anomaly enhancements showing you a |
|
| 98:17 | examples of these things. But now going to show you like trying to |
|
| 98:22 | a comprehensive collection of anomaly enhancements over same area. So you can at |
|
| 98:30 | contextualize. Right so in the gulf Mexico here is here is uh um |
|
| 98:39 | anomalies over the gulf of Mexico and gulf coast. And it's free air |
|
| 98:47 | land and water. So it's dominated topography. Remember so you can definitely |
|
| 98:57 | like offshore. You definitely see the you know the shelf edge six B |
|
| 99:06 | that runs right through here topographically right is the six BB escarpment here. |
|
| 99:14 | then of course you have higher The Watchtower Mountains Appalachians here. So |
|
| 99:20 | look at the free air you can those shapes but it's not a one |
|
| 99:26 | one which means there's other things going But certainly you can see some topography |
|
| 99:33 | over the rio grande delta. All produce all deltas produced. Free air |
|
| 99:39 | . It's just the way it So we'll go back and look at |
|
| 99:42 | topography again one more time. So , but there's other things going |
|
| 99:49 | There's big anomalies where it's pretty like, like down through here, |
|
| 99:56 | , And let's just figure that So this is free air, free |
|
| 100:02 | and this is boogie land, free marine. Now, when you get |
|
| 100:10 | from a contractor over marine areas, know, you'll get it as free |
|
| 100:15 | , they might make a bouquet, they'll certainly give you free air. |
|
| 100:21 | again, I said, as I earlier, that's because of where the |
|
| 100:25 | are. Um so this low that's the east texas basin. There's |
|
| 100:35 | basin up here called the north Louisiana . There's a basin right here Beneath |
|
| 100:41 | high, it's called the Mississippi salt . And then in here there's the |
|
| 100:48 | Georgia rift that's between this high and edge right here, there's a basin |
|
| 100:55 | here called the Apalachicola. There's a here called the Tampa basin. Your |
|
| 101:00 | sub basins. Of course, this area is just, you know, |
|
| 101:05 | gulf of Mexico gulf coast salt basin down through here, right, and |
|
| 101:12 | southern southern southern texas is kind of the whatever the western extent of the |
|
| 101:20 | gulf basin. So now here's a land boogie marine. So what does |
|
| 101:30 | mean that means this has all been sort of uh, the, the |
|
| 101:40 | at the topography, whether it's the bottom or land that's been minimized to |
|
| 101:47 | the contribution of topography. So of , you you can see where all |
|
| 101:56 | a sudden you've got this big gravity . Well, what did I say |
|
| 102:00 | the second highest density contrast? When say that was I said topography was |
|
| 102:11 | highest density contrast. It's the second . Water. No water is the |
|
| 102:23 | . The surface of topography, whether beneath water or air, it's still |
|
| 102:31 | highest density contracts. What's the second density? What's the second most? |
|
| 102:41 | right. That's what you're seeing Long wavelength here. You see this |
|
| 102:46 | high, that's because this is ocean out here. Okay. And you |
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| 102:52 | the big low beneath the Appalachians and Ouachita mountains. And over here, |
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| 102:58 | because the mojo is depressed beneath these ranges beneath. Right? It's pushing |
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| 103:03 | . So the second most prominent density is the base of the crust. |
|
| 103:09 | mojo because That that's a point for contrast. And so you can see |
|
| 103:17 | long wavelengths because up here, it's km, 30 40 km down out |
|
| 103:25 | . I mean, the water bottom at 3km. The section over here |
|
| 103:31 | about seven kilometers that's 10 crossed is six kilometers, that's 16. So |
|
| 103:39 | about 16 or 17 kilometers to the of the crust here and you're about |
|
| 103:45 | that up here. So that is dominant. And so that's why we |
|
| 103:53 | to residual eyes these data because we want to be, I mean, |
|
| 103:57 | swamped, right, we got rid the effect of topography, but now |
|
| 104:01 | have the second biggest contrast, which this long labeling thing we got to |
|
| 104:06 | rid of. So we can try a polynomial. Here's a second order |
|
| 104:13 | . And what does that residual look ? Well, it looks like |
|
| 104:17 | So you have, we have actually quite a bit of this down with |
|
| 104:23 | , you know, starting to see features pop out. So so that |
|
| 104:27 | it's drive from this second order kind a parabolic sort of feature going through |
|
| 104:36 | produces that. What about a third polynomial? Now it's low here, |
|
| 104:43 | here. The Appalachians and low out . What does that look like? |
|
| 104:47 | pretty good. That's even that's nicer write. So we're teasing out and |
|
| 104:55 | these anomalies, we can we can , you know, we can be |
|
| 104:58 | of confident that these are anomalies produced geology beneath the surface. They're not |
|
| 105:04 | or not, there's probably some surface , but there's probably, you |
|
| 105:08 | I would imagine all this stuff out , sir. Okay, now what |
|
| 105:15 | a convolution filter? Right, so is a regional generated by a three |
|
| 105:22 | 3. Remember I showed you the manual convolution stuff. So this is |
|
| 105:28 | three by three matrix, which has over this grid 500 times convolution filters |
|
| 105:36 | not that effective. That's why you to do a lot of and this |
|
| 105:40 | the regional, Let's subtract that? is one ugly map. I |
|
| 105:46 | I'm not crazy about this. I it's just I think it's too |
|
| 105:52 | I mean, I it looks almost it's a single, you know, |
|
| 106:00 | looks like those maps of it was sat maps looked at for the |
|
| 106:05 | you know, it's like one you know, way wavelength percolating through |
|
| 106:12 | . Everything else is kind of So you can't see some things |
|
| 106:19 | You see these these blue lows down in the gulf of Mexico, these |
|
| 106:25 | features I'm tracing. Do you see I'm tracing down here? Yes, |
|
| 106:32 | are actually extinct spreading centers over These are extinct fractures on this way |
|
| 106:39 | the opening of the gulf of Okay, What about a nine x |
|
| 106:47 | convolutions? So this is actually a , would be like a bigger ring |
|
| 106:52 | same number of iterations for this What does that look like? That |
|
| 106:57 | just not. This is not any at all. This is pretty darn |
|
| 107:01 | . I wouldn't use this at But I just want you to get |
|
| 107:04 | sense for what what what can you know, like don't do this |
|
| 107:12 | home, kids kind of stuff. Let's look at some continuations here is |
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| 107:18 | 20 km upward continuation of the bouquet gravity. Okay, let's subtract that |
|
| 107:26 | the data now? This? This this is very nice. I think |
|
| 107:33 | is very nice. What what do think? I shouldn't say I should |
|
| 107:37 | you what you think first. Sorry that. I I like it but |
|
| 107:43 | like those other ones better. Like the last two that we looked at |
|
| 107:49 | the bouquet like land and marine. think that was my favourite one. |
|
| 107:57 | one. I'm sorry. No that I like that one. I feel |
|
| 108:03 | that one made the most sense to . But you wanna but you want |
|
| 108:08 | enhance, you wanna remove these long so you can cheese out some of |
|
| 108:14 | effects of these shorter wavelength anomalies in . That's that's why we do |
|
| 108:20 | Right? I mean so I'm so saying that this long wavelength these broad |
|
| 108:26 | and highs and stuff. I'm saying those are those are those that general |
|
| 108:32 | is high to low that's a function the crustal thickness. Now we wanna |
|
| 108:38 | know if you want to interpret the that's in the in the basins that |
|
| 108:43 | know you you you sort of you to isolate those anomalies and this big |
|
| 108:50 | broad field that's going through here is something that we should try to regionalize |
|
| 108:57 | take away. That's the idea. the idea. So that's why the |
|
| 109:04 | or apply was regional. We're left this. So this is the |
|
| 109:10 | So trying to tease out some of features in here. 3rd, |
|
| 109:16 | 3rd order. Another residual three Another residual But this is this is |
|
| 109:25 | little over the top for me nine nine. Another residual. This just |
|
| 109:30 | noisy to me Now a different kind residual instead of convolution or polynomial. |
|
| 109:38 | is an upward continuation. So in case 20 km and the residual I |
|
| 109:45 | I mean to my eye the reason like continuation is because their broadband and |
|
| 109:52 | don't like to ban limit data because think you get artifacts from it. |
|
| 109:57 | so this you know we're kind of isolating even seeing you know seeing some |
|
| 110:05 | in in the basins themselves. Some are popping out. So this is |
|
| 110:10 | Mississippi salt base and there's a reason it's a high instead of a |
|
| 110:13 | This is the Wiggins arts but it like now it's just like it's being |
|
| 110:16 | somehow but you know um and then then northern Louisiana salt base and you're |
|
| 110:22 | starting to see the shape of See it's actually a continuation of the |
|
| 110:27 | texas basin kind of wraps up around and then um. Yeah so. |
|
| 110:32 | . Is there another Alright five kilometers Virginia. So just a little |
|
| 110:36 | So when you when you upper continue 20 it's very smooth if you only |
|
| 110:43 | five km it still has a lot the it still has a lot of |
|
| 110:47 | on it. So what does this like when I subtract it. But |
|
| 110:52 | this is this is interesting. I know if it's too much but now |
|
| 110:57 | really saying some really detailed being you , kind of pulled out of this |
|
| 111:03 | , there's some really interesting features starting pop through that you wouldn't otherwise see |
|
| 111:09 | they just be swamped by the You see this is something I didn't |
|
| 111:14 | a point of. But this map plus or minus seven mg. So |
|
| 111:18 | is the total range and this thing about 15 mg. But if we |
|
| 111:22 | back to the original boogie math, I'm sorry I went the wrong |
|
| 111:32 | We go back to the original boo app. The total range goes from |
|
| 111:42 | a is over 230 million. So got it's got a huge range in |
|
| 111:49 | and every residual reduces that second order . We got like -60-83 order |
|
| 111:57 | we're at 57-60. So it's a bit less. Um convolution, this |
|
| 112:04 | only plus or -9 and then the order problem, this is just plus |
|
| 112:08 | minus five. So that's you there's nothing in this thing. 20 |
|
| 112:14 | were plus or minus about 20 and the five kilometer up situation were plus |
|
| 112:20 | minus whatever. Eight. So this what I mean when I say well |
|
| 112:26 | sort of flattening the data. We're taking out that big big swells of |
|
| 112:33 | and lows and we're trying to see , what subtle features lie on top |
|
| 112:40 | those that we can enhance. That's idea. Does that help? |
|
| 112:47 | it does. Okay, alright, . Um okay, whatever. So |
|
| 112:52 | a 200 kilometer low pass. This so this is the regional, this |
|
| 112:58 | still, this is uh well over million. So the residual, it |
|
| 113:04 | from about 20 to 20. Now I look at this, what I |
|
| 113:10 | that's different than the continuation. So there's a 200 km Lopez. |
|
| 113:15 | look at the continuation again, I at this continuation and I see there's |
|
| 113:23 | wavelengths but there's a lot of short . When I look at this this |
|
| 113:29 | , I'm starting to see like a kind of like this, I see |
|
| 113:35 | sort of same wavelength everywhere. Do see that? Or am I just |
|
| 113:41 | imagining it? No, I I can kind of see what you're |
|
| 113:48 | . Can you explain what low pass again? Like in So it's saying |
|
| 113:55 | so 200 the cut off is 200 . Anyways, wavelengths greater than 200 |
|
| 114:01 | are cut off. Okay, so is where 200 kilometer and smaller. |
|
| 114:07 | ? I mean, yeah, so whatever this distance from from top of |
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| 114:14 | to here, that's probably 300 I would get something like that. |
|
| 114:18 | , let's go two degrees. This 200 kilometers right here. 32 32 |
|
| 114:23 | 200 kilometers. So when I look this, what what I I would |
|
| 114:28 | at this and I would say this been banned limited and I know it |
|
| 114:32 | because if I go, if I this anomaly here, I know what's |
|
| 114:38 | here in the west, the distance that guy, it's almost the same |
|
| 114:45 | the way up here. That distance about the same here. Distance is |
|
| 114:50 | same over here. It's the same here. The same over here. |
|
| 114:53 | the same. It's the same. the same. I can see that |
|
| 114:59 | . I mean, it's almost like operator is dominating this map. Do |
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| 115:06 | do you see what I'm saying? . And the way you explained |
|
| 115:10 | Yes. And then I do a km of all past This is now |
|
| 115:21 | , right? 50 km is like this is what I call pizza |
|
| 115:26 | right? All the little pepperoni because , this is just all you're seeing |
|
| 115:33 | here is the operator, you're not looking at data anymore. You |
|
| 115:38 | you see what I mean? Excuse me. So what else do |
|
| 115:51 | have here? First vertical derivative. , so this is the first vertical |
|
| 115:58 | . Remember was sharpened, they sharpened . So all these, all these |
|
| 116:06 | are kind of like, you uh really delineated. But then some |
|
| 116:12 | , now this is satellite gravity down . Satellite gravity can be noisy. |
|
| 116:21 | onshore stuff is all stuff that's um the open file. Great. So |
|
| 116:35 | , it looks kinda noisy down I mean you can still see the |
|
| 116:40 | centers and the fractures on and then big deep salt bodies over here. |
|
| 116:47 | what these blues are, big thick salt. And then this this line |
|
| 116:53 | that's called the cretaceous carbonate shelf shelf . And of course the Appalachian piedmont |
|
| 117:00 | down in through here when the Piedmont's here and it the uh our coma |
|
| 117:08 | . That's our comics. But I am not crazy about derivatives for just |
|
| 117:13 | same reason because what derivatives do if have noise if you have, you |
|
| 117:19 | , warts in the data, it's gonna, those are gonna shine. |
|
| 117:24 | then here's the second vertical driven which think completely there's nothing you can get |
|
| 117:30 | the marine side. You might be to if you zoom in. Maybe |
|
| 117:34 | some features that are important if you're locally like just at the east texas |
|
| 117:39 | or something like that. But I mean I I seldom do, |
|
| 117:47 | don't ever do second vertical derivative Okay. And this signal. So |
|
| 117:53 | is kind of a, they're kind strange to look at. But so |
|
| 118:00 | here's the east texas basin and you see it's shaped with the Louisiana salt |
|
| 118:06 | . This is the Sabine uplift. , it's interesting. I have a |
|
| 118:16 | time with this. This enhancement. remember the analytic signal Is the root |
|
| 118:27 | the sum of the squares of all directional derivatives X, Y&Z. And |
|
| 118:36 | the one for Magnetics at least doesn't what the inclination is. We're still |
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| 118:42 | at gravity. All right, here's total horizontal gradient and I actually like |
|
| 118:55 | maps because they tracked edges of things I know alan signal is supposed to |
|
| 119:00 | that, but I think horizontal gradient does a better job of it. |
|
| 119:05 | mean you can definitely see the edges sources. So where there once was |
|
| 119:11 | high. Now there's two edges on sides of it, If that makes |
|
| 119:19 | . So the six b escarpment, pops right out. You know, |
|
| 119:27 | is this is Jackson Dome right I think. I'm sorry, this |
|
| 119:31 | Jackson Dome right here. And then Monroe uplift where there are a bunch |
|
| 119:38 | intrusions and finally the tilt drip, is a pretty crazy map to look |
|
| 119:50 | . Um People use them a You can, they can definitely correlate |
|
| 119:56 | a lot of, a lot of and in fact, you know, |
|
| 120:01 | lot of those features I was pointing up in here in the east texas |
|
| 120:04 | are just really prominent with this toad . But this is kind of, |
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| 120:09 | I said, it's a weird it's the it's the this angle and |
|
| 120:15 | is the inverse tangent of the vertical over the route? Over the horizontal |
|
| 120:22 | rather? Yeah, so it's just ratio, so what does that |
|
| 120:27 | You think of the ratio of vertical horizontal? So if it's a positive |
|
| 120:38 | , that means that it's um That that does it does it mean that |
|
| 120:51 | vertical gradient is greater than the horizontal and if it's negative doesn't mean that |
|
| 120:57 | horizontal is greater than the vertical. think that's what that means. So |
|
| 121:05 | vertical gradient is higher than the horizontal . That means there are sharp |
|
| 121:13 | Um But the horizontal gradient is stronger the vertical. That means that means |
|
| 121:21 | it's a low amplitude anomaly because the the vertical grade is low, that |
|
| 121:29 | the amplitude of the anomaly is but the horizontal grain is high. |
|
| 121:33 | means that yeah, that means that the change in X and Y is |
|
| 121:42 | than the change in Z. So a little amplitude anomaly. And if |
|
| 121:49 | vertical gradient is higher, that means the change in the vertical. |
|
| 121:55 | That means the change in amplitude is Z is faster than the change in |
|
| 122:01 | . See what I'm trying to work . Does it make sense? It |
|
| 122:06 | , Yes. I mean, I look at these things and trying to |
|
| 122:10 | out what does what does this enhancement physically what what's the physical meaning of |
|
| 122:17 | ? I mean, I think this a really interesting map because it has |
|
| 122:21 | lot of a lot of features in . And I know where some |
|
| 122:26 | I know where the basins are and know like this is the Mississippi salt |
|
| 122:32 | right here and this is the Wiggins through here. But they're really well |
|
| 122:39 | and yeah, it's just interesting. is the cretaceous carbonate shelf edge coming |
|
| 122:46 | through here wrapping around that way. , what's next? Okay, so |
|
| 122:53 | can look at magnetic anomalies over over the basin now, here's against |
|
| 122:59 | and here's the total field anomalies M. I total minutes. So |
|
| 123:04 | is just just the core field So very long wavelengths down here all |
|
| 123:20 | you really. So this is The of Mexico is very deep down. |
|
| 123:27 | about over here in the West, about 15 or 16 km from |
|
| 123:33 | from the sea surface to the base the sentiments over here in the |
|
| 123:42 | right around in here, it's what did I say? It sucks |
|
| 123:49 | or 7 - 9, 7 So no maybe nine collars to the |
|
| 123:55 | , something like that. They had big covenant backs of florida and on |
|
| 124:03 | side of the thing. But these still pretty long wavelengths. So it's |
|
| 124:09 | pretty deep through here. All these really long wavelength and you have long |
|
| 124:13 | even going up into here into the texas basin and over here and then |
|
| 124:22 | starts to get smaller shorter in wavelength of through here and up up into |
|
| 124:28 | and then they get really short right . So this is just a basement |
|
| 124:31 | right beneath the surface right here, up in here waterway links up |
|
| 124:37 | So again viewing magnetic data is a way here's a Sabine uplift. So |
|
| 124:45 | definitely some you know there's cretaceous cretaceous um volcanic pipes that come all |
|
| 124:52 | way up from central texas down through into the Sabine and underneath the Monroe |
|
| 124:58 | . And of course Jackson Dome. . Okay so this is the reduced |
|
| 125:07 | pull. So if I go back forth you'll see the anomaly shifts. |
|
| 125:11 | they're gonna shift to the south Now when I hit it again we're |
|
| 125:15 | shift to the north. You see you see how they change. Yeah |
|
| 125:25 | get like dinner. So pay attention this one right here. This is |
|
| 125:32 | the Houston anomaly by the way that that we're sitting beneath right now. |
|
| 125:38 | what happens to that when I when go to R. T. |
|
| 125:41 | Okay. Yeah. See shift you have to just look at specific |
|
| 125:48 | . If you look at these in Sabine uplift these little ones and their |
|
| 125:54 | , you can see how they shift . So yeah in any case right |
|
| 126:03 | that's that's done that. Now when first made these maps I was thinking |
|
| 126:10 | man there's bad data. You see east west striations down here. Yes |
|
| 126:18 | bad data. What's happening is the that this is this day this process |
|
| 126:25 | done in the frequency domain or And there's there's some spike in the data |
|
| 126:32 | there and it's causing that rippling through . But I'm gonna leave it in |
|
| 126:38 | so you can see how pervasively and awful they can get. Alright, |
|
| 126:43 | here's the second order polynomial trend and the reason the residual from that polynomial |
|
| 126:54 | . So that doesn't look much First of all, let's go back |
|
| 126:58 | our CBS. This range is basically or -350 Nana testing. So this |
|
| 127:06 | is still, you know, plus -300 basically. Alright, 3rd order |
|
| 127:15 | , the residual is still about the . Just a different shape. That |
|
| 127:20 | from that residual, they're not much . These residuals are very low |
|
| 127:27 | The third is Goes from -22-57. second goes from minus. Yeah, |
|
| 127:34 | almost the same amplitude. So there's aren't having much. Let's look at |
|
| 127:40 | in it. So I just did same operators on all these as I |
|
| 127:43 | on gravity. The same operators. this convolution, this is ranging from |
|
| 127:53 | to 40 basically. And um if subtract that here's the residual and this |
|
| 128:00 | , I don't care for this at . This goes from minus 1 50 |
|
| 128:03 | 1 70. But it's um it's I think it's just uh you |
|
| 128:10 | , it's just suffering from this convolution . What about the nine x |
|
| 128:16 | This is gonna be really horrible. sure. Yeah, this is this |
|
| 128:21 | not even data. Okay. Um continuation regional. So again, this |
|
| 128:29 | kind of nice. See I I think a 20 km upper |
|
| 128:33 | you just can't go wrong with I mean it's not going to hurt |
|
| 128:36 | data at all. It's not going prove. I mean these artifacts are |
|
| 128:39 | the R. T. P. not in this, not a result |
|
| 128:42 | this operation. five km is a close. We'll probably see some |
|
| 128:51 | What's the range on this one? still higher range um five km. |
|
| 128:58 | it's not that bad. I frankly don't like it that much. But |
|
| 129:03 | mean, you know, because it's to like isolate, you know, |
|
| 129:08 | know, the you're probably going what the heck is he looking at |
|
| 129:14 | I'm doing is I'm just looking at the these anomalies and uh I'm seeing |
|
| 129:23 | , you know, you kind of the repetition of this operator through |
|
| 129:28 | It feels like it's it feels like kind of being overly processed so you |
|
| 129:36 | disagree and that's fine. But but are some pretty subtle features that are |
|
| 129:42 | through. I don't really know if ever noticed this one before. So |
|
| 129:47 | interesting. Okay, What's next? here's the Lopez this is and what |
|
| 129:54 | that look like? I could live that even though I don't like band |
|
| 129:59 | data, but I could live with ? That's that's okay because it looks |
|
| 130:02 | it has a broad spectrum of you know, it has a lot |
|
| 130:06 | wavelengths in it, I don't like when it looks like there's just a |
|
| 130:10 | single wavelength, you know, through . This is gonna be horrible. |
|
| 130:16 | , this is just not unusable. mean it's just no good um First |
|
| 130:23 | derivative. Now this noise, you this bad data down here, This |
|
| 130:29 | is just yeah, it's just wrecking the map down there and a little |
|
| 130:36 | up here too. And yeah, one thing you can do to try |
|
| 130:41 | get rid of that stuff is to make a huge grid like four times |
|
| 130:47 | size of your map because a lot times these features are on the edges |
|
| 130:53 | you can still zoom into your to work area and you're you're okay. |
|
| 131:00 | is not I mean setting aside these . This is not horrible because there |
|
| 131:06 | , you know, there are different through there are different features and it |
|
| 131:11 | have some character to it. the analytic signal, you know, |
|
| 131:19 | I mean besides the noise um I'm I don't know, I've never really |
|
| 131:28 | been crazy about L a signal to what does the horizontal gradient look |
|
| 131:35 | Oh, oh I see, this what this. Okay, so this |
|
| 131:41 | right, The Alex signal is the the is the root of the sum |
|
| 131:47 | the squares of all the directions. here I just said, well let's |
|
| 131:51 | , what would you do to try figure out what's going on with |
|
| 131:55 | And you don't see you don't see artifacts in the in the in the |
|
| 132:01 | . X. Component. So this derivative with regard to X. Because |
|
| 132:09 | the north south trends are enhanced. means the derivative is going from west |
|
| 132:15 | east. Does that make sense? , I was wondering why everything looks |
|
| 132:20 | vertical. That makes complete sense. down here everything is is the driver's |
|
| 132:26 | from south to north. So the is what's polluting that uh that analytic |
|
| 132:33 | , man. And then here is total horizontal grating. So the dy |
|
| 132:40 | is bad but still, I mean horizontal great is a good enhancement. |
|
| 132:44 | really does a nice job of showing edges very nicely. And then |
|
| 132:54 | the tilt derivative. Again, very a fascinating map because of the so |
|
| 133:02 | derivative. Here's what Children, it's to me to to uh an A |
|
| 133:08 | . C. A reflection data. see everything has gotten basically the same |
|
| 133:14 | range. See what I'm saying? , it looks very psychedelic to |
|
| 133:25 | totally. I mean, let's go to the other A G. |
|
| 133:29 | I mean the other the other the toe derivative. You see that everything |
|
| 133:38 | the same min max range. It's plus or minus one. And so |
|
| 133:42 | all been sort of, you like when you A G. |
|
| 133:45 | A seismic data, everything is like same amplitude, Right? That's what |
|
| 133:53 | kind of an A. G. . For financial field data. You |
|
| 133:59 | it? Okay, Let me go down to here now. Very |
|
| 134:05 | Yeah. No, they're pretty cool . I like, I like, |
|
| 134:09 | like derivatives. I think they're, think they're fun. Um I |
|
| 134:16 | and I had one of my students use them to do to do uh |
|
| 134:24 | estimation for the all of the east of north America and all of |
|
| 134:32 | you know, Western africa, You , the, not sub Saharan |
|
| 134:36 | but uh Saharan africa, West To do that. Anyways, this |
|
| 134:44 | the last slide I see. Um we're like, again, really super |
|
| 134:49 | . I mean, this is You're not asking me any questions. |
|
| 134:53 | I'm going to go all the way to the beginning and then we can |
|
| 134:56 | through it again because maybe you'll see after you thought about it that you |
|
| 135:01 | have a question about. In I'll go back to. In |
|
| 135:06 | I'll go back to the 1st 1st . Can I take a quick like |
|
| 135:17 | minute break? You can take a minute break. 10 minute break, |
|
| 135:22 | you want to do. Okay, be right back. Okay, |
|
| 135:34 | so this is this morning, we're says, why don't you say saturday |
|
| 135:39 | . M. O. That's the one. Alright, so this is |
|
| 135:45 | morning. Um and this was all boring uh instrument. I really probably |
|
| 135:51 | have said something like that. so instrumentation acquisition, processing and we |
|
| 135:59 | at gravity instruments, the L. . R. Meter, which is |
|
| 136:02 | relative measurement. And is there a spring, you know? So he |
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| 136:08 | out how it actually works And right, basically let me just say |
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| 136:21 | zero length. Okay. The line force plotted as a function of sprinkling |
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| 136:28 | through zero. That's why it's called length strings, strings spring in the |
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| 136:37 | . Then we looked at C. five and there's a little picture of |
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| 136:42 | there, the micro G instrument. and then borehole instrument and it measures |
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| 136:49 | directly, its limitations and is its and angle, of course temperature. |
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| 136:58 | Then we looked at the bottom hole uh then this study that was published |
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| 137:06 | in '09 about how accurate they Less than 30 micro micro gals. |
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| 137:15 | who cares, I'm sorry. All . So then we look at magnetometers |
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| 137:20 | then so there's the flux gate which the first one invented by victor vacuum |
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| 137:25 | World War two times. And and how that works basically the opposite, |
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| 137:35 | wound coils counts each other out at , but then when it feels imposed |
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| 137:41 | they're obviously wild, there's differences and that difference in mechanization of that of |
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| 137:49 | permeable substance will produce a signature that's to the to the field strength. |
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| 137:59 | we look at proton precession which kind works like a top. It's it's |
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| 138:05 | it's basically uh how does it work the when, when, when the |
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| 138:16 | are pulsed, then when they Uh the the material processes and then |
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| 138:25 | is, and it's precisely as a of the field strength. And then |
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| 138:31 | and that's what that looks like. the optically pumped where basically there's a |
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| 138:36 | and of course, these two these residents types they work on on a |
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| 138:44 | level. And of course, uh then the the proton procession is order |
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| 138:50 | magnitude better than the flux gate. optical pump is another order of magnitude |
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| 138:56 | . And it basically works on these levels that uh that will um will |
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| 139:06 | a radio frequency that's proportional to the full strength when they when they return |
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| 139:11 | their their energy loans. Then we about survey planning and the objectives and |
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| 139:23 | the expected target response. The methods surveys uh instrumentation for that. I |
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| 139:32 | a I found a slide where I a question curiosity question slide 29 on |
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| 139:39 | lecture, we're currently looking at. I'm just curious on like that point |
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| 139:48 | the indian ocean. And then like high point right there by the |
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| 139:51 | like what what's causing or like what's interesting about the indian ocean that that |
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| 139:57 | is so low. And then like going on on to the other |
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| 140:01 | So these are G oil anomalies. ? So remember they're the echo potential |
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| 140:08 | . So this is like Grady Amateur Grady Amateurs match a print level. |
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| 140:14 | , this is actually measuring the grain the field. So but these are |
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| 140:21 | long wavelengths. So that means they're very deep I think. And and |
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| 140:26 | all that means is that there's high mass deep in the earth that's producing |
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| 140:35 | high and low density here. what's going on here? Where there's |
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| 140:40 | bunch of sub ducting slabs all around . So those slabs um are they |
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| 140:50 | mass there? Colder? But that mean it seems colder. Would make |
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| 140:56 | low. Um Where's the geek Do we look at the G. |
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| 141:02 | today? Was that yesterday? I joyed was yesterday. Yeah, I |
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| 141:12 | it was I want to look at . Let's just 29. Uh |
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| 141:21 | Mhm. I'm going to see I at this and I was just thinking |
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| 141:32 | those those anomalies lined up well with G. Oid. Yeah, they |
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| 141:41 | . So, So here's that low and the low here this is the |
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| 141:56 | is that high I think what Yeah. So that's what so these |
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| 142:10 | and and it makes sense? I the geode anomalies. Those are produced |
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| 142:15 | deep density sources, you know, beneath the atmosphere in the in the |
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| 142:23 | mantle. And uh you know, don't know what's going on down |
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| 142:29 | I know that there's a lot of zones here, but there's also a |
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| 142:34 | high down here in South Africa. and and in the southern ocean, |
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| 142:42 | southern south atlantic and stuff. So not clear to me what's producing these |
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| 142:49 | . Um There's a big high along Andes, but then there's a low |
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| 142:56 | the Canadian rockies and there's a low , but the Himalayas, you |
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| 143:03 | So yeah, I'm afraid. I know the answer to that. |
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| 143:12 | I was just curious because it's just like pronounced like it's such a huge |
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| 143:16 | . I thought it was interesting. making a note to slide 29. |
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| 143:34 | answer you. I'll find out. , well I'm in, I need |
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| 143:45 | and then I skipped over a bunch stuff. I don't know, was |
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| 143:48 | anything else that you between here? looked at the surveys and the airplanes |
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| 143:53 | everything. And the mapping surveys. really because when I took remote sensing |
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| 143:59 | the Gps and lidar, they hit of this stuff like kind of |
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| 144:04 | Um So I remember this most of from those previous classes. Okay. |
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| 144:12 | I went through this famous mhm brutal study. We talked about Magnetics. |
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| 144:22 | and her first name was jennifer, the way, that hair. Um |
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| 144:27 | yeah, it was jennifer hair. had looked it up. Oh you |
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| 144:31 | okay okay yeah. Alright then. . So you're okay. And then |
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| 144:41 | looked at magnetic surveys and the aircraft that little video of the drone. |
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| 144:50 | then this little case history on deep in the Iberian abyssal plain. Uh |
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| 144:58 | looked at some upper mantle rocks and floor spreading anomalies. And uh yeah |
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| 145:09 | was fun instrument summary survey planning data . So you went through all the |
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| 145:16 | corrections. You get all this Yeah. I mean yeah I'm I'm |
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| 145:23 | master at it. No but it sense to me and everything seems to |
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| 145:31 | so um I'm just a great I guess so. And then we |
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| 145:36 | about title corrections uh and drift which very simple. And then the latitude |
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| 145:48 | , uh elevation corrections, boogie And then of course this very this |
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| 145:55 | very important what the point of being corrections and then old school terrain corrections |
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| 146:03 | at most correction for a dynamic how that works. The magnetic corrections |
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| 146:11 | controversies around that day Colonel. And is how Darrell changes in different parts |
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| 146:18 | this little case history on dire no micro pulsations, magnetic storms, the |
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| 146:28 | flow for maggie ridiculously simple uh core and then of course the field parameters |
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| 146:37 | and then the dynamo which is very . Uh This little simulation down |
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| 146:43 | I like that paper a lot. then another mag sat and then showing |
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| 146:51 | uh the uh whenever the frequency can the the wavelengths in the data um |
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| 147:02 | variation. There's not this is an paper but there's not a lot of |
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| 147:08 | of this stuff. And then of we summarize the external and internal sources |
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| 147:15 | magnetic field changes. Then I just uh polar wander because that will be |
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| 147:24 | to think about later. Just introduce topic. So that was um isn't |
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| 147:32 | north pole like currently around, is like um for like the pole wandering |
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| 147:38 | isn't the north pole currently like in or something they're saying okay but it's |
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| 147:49 | this is not talking about that these polar wander paths are when you do |
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| 147:56 | Magnetics and stuff, you always assume the pole is the same as geographic |
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| 148:05 | because you don't know you don't know the document, you can work on |
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| 148:09 | declination in some cases depending on the . So I will say that for |
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| 148:16 | . But yeah these so some of some of those do include that. |
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| 148:23 | this is just basically if you calculate rotation pole between africa and south |
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| 148:29 | you know, you can you can if you say that one, if |
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| 148:34 | say that some point here right it was right there. I make |
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| 148:39 | assumption at some time in history then can then then I can then I |
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| 148:46 | calculate the angle from there to there some pole that will rotate this point |
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| 148:53 | there that's just whatever spherical trigonometry. and Spirit of trigonometry is actually easier |
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| 149:05 | plain or trigonometry. Do you know ? You know why? No because |
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| 149:15 | you assume a radius of one then the angles the angles um the arc |
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| 149:31 | lakes on the surface are equal to angle in radiance. So that that |
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| 149:41 | simplifies everything. And then you all gotta do is just scale everything up |
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| 149:46 | your diameter when you're done doing your anyways. Yeah so Spirit of technology |
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| 149:52 | not that difficult. Okay and then looked at okay so here is our |
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| 149:59 | we just did the second time and we looked at and only enhancements, |
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| 150:05 | signals um you know different combinations of and amplitudes and then we talked about |
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| 150:13 | hands with the different kinds regional residual , derivatives convolution frequency domain and of |
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| 150:21 | there's things you can do with magnetic RTP or pseudo gravity both of which |
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| 150:26 | not crazy about. Um Then we we did um But don't take my |
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| 150:33 | mean look someone might convince you otherwise you know I'm just telling you what |
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| 150:37 | think but I'm not I'm not the jury and executioner of R. |
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| 150:43 | P. Um in any case. ? So then we talked about you |
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| 150:47 | some examples of residual regional separation using methods and some so just an example |
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| 150:57 | south of Houston And then write this idea of convolution. So this would |
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| 151:04 | this this here is a three x matrix and This one would be uh |
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| 151:13 | this is also a three x 3 it's it's two different this one here |
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| 151:19 | a few, it's a five by in other ways but you can you |
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| 151:26 | , so I tried a three by and the nine by nine on the |
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| 151:30 | gulf of you know gulf coast data then a lot of this stuff is |
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| 151:35 | old school but um I'm showing you older stuff not so much as I |
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| 151:40 | it's important to learn but I think important to understand um kind of like |
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| 151:47 | all these ideas are rooted in, know um because many of the stuff |
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| 151:53 | do today with data, it's just in these older ideas and so I |
|
| 151:58 | there's been newer newer ways to do like you know, D. |
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| 152:03 | S. For train tracks for But but the idea the idea of |
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| 152:09 | that goes right back to the you know zones and compartments and things |
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| 152:15 | that. So it's important to you know just like it's important understand |
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| 152:20 | the potential is even though you might making residual maps and you might be |
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| 152:26 | data and you're not really you're not using, you know you're not sitting |
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| 152:32 | there calculate, well let's see there's much work is involved move this blah |
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| 152:36 | blah. You're not doing that. it's it's important to understand um what |
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| 152:43 | know what the potential is and it's to understand where these methods, how |
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| 152:47 | developed over time and and where we're now. So that's that's what I'm |
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| 152:52 | to just I guess that's why a of and you know a lot of |
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| 152:57 | anyway, so that's why I'm just you this but you can see the |
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| 153:00 | thing in modern examples and I do same thing. It's just that a |
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| 153:03 | of stuff was done a long time too. So yeah, so I |
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| 153:07 | showed this example from Nettleton and then this neat little I think I think |
|
| 153:14 | bile this from forward uh this looks dot art and that and fuller publishing |
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| 153:21 | a classic paper in a two volume called mining geophysics SDG publication maybe three |
|
| 153:30 | and then the conversion from space to . And then we looked at band |
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| 153:35 | data. And then of course I all these examples from behind his |
|
| 153:40 | It's actually Heinz von freeze and say rather. And then this example of |
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| 153:48 | Richmond Basin and how you can isolate this this kind of a little confusing |
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| 153:57 | filter case in in Western Canada. then we looked at continuation. That's |
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| 154:06 | favorite way to uh treat data. know. The only thing I ever |
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| 154:12 | to data is that I do a on gravity data. Otherwise I just |
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| 154:18 | everything else straight up. I mean mag dated because I've been doing for |
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| 154:23 | long. I know if I know inclination declination I just know where the |
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| 154:27 | should be looking at the anomalies. maybe that I'm putting my bias on |
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| 154:33 | because of I have a lot of doing this stuff and I just look |
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| 154:38 | these maps and I can just kind figure out what's going on. But |
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| 154:42 | so I hope you these examples ah the point that are meaningful. And |
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| 154:49 | that's why I was always looking at scale and the contour intervals. So |
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| 154:54 | size of anomalies and you know always wavelengths and stuff because I think I |
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| 155:01 | you know if you want to infer from these days you have to understand |
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| 155:06 | know what sort of wavelengths and amplitudes can associate with different sort of geologic |
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| 155:15 | . And that's very important. And gonna get dig into that more. |
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| 155:22 | yeah so let's see I did So this is a beautiful example. |
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| 155:27 | understand that completely. But you know when I get those single maps, |
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| 155:31 | don't see this stuff in it. don't see it. So you know |
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| 155:37 | it's just my problem maybe I need do some of these death estimates with |
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| 155:40 | go and get better at it. then there's this study which this um |
|
| 155:47 | know this study in china where there's lot of, there's a lot of |
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| 155:52 | and stuff in here and I don't . Yeah, I mean it's pretty |
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| 155:57 | second here again, this is this is the the A. |
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| 156:01 | C. Everything's bouncing between men's and . It's all just, you |
|
| 156:08 | it's like everything is enhanced to So like these anomalies which are higher |
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| 156:14 | are now equal to everything else, know, and he's very low altitude |
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| 156:20 | are now equal to everything like up here, you can barely see it |
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| 156:23 | all of a sudden now. so I think in a way that's |
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| 156:27 | because you can sort of, I you kind of kind of refer back |
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| 156:32 | the other one, but it's important then this one. Yeah. |
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| 156:38 | And then we looked at the evidence deaths derived from that, looked at |
|
| 156:44 | transform and how that works to, basically reconstruct, you know, the |
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| 156:52 | . But using these, these um do you call it? The coefficients |
|
| 156:58 | it, coefficient versus so this is scale vertically. And it's the value |
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| 157:04 | the coefficients to produce that for every level. So summing these all up |
|
| 157:10 | produce that the sum of the waves are associated with these these values for |
|
| 157:18 | scale will produce that, that's the . So it's discreet ties ng the |
|
| 157:25 | the the anomaly uh in this format different ways, does that make sense |
|
| 157:32 | you? Yes. Okay. And we looked at our teepee and I |
|
| 157:40 | that much good to say about Um I know that folks like it |
|
| 157:48 | so I know I'm fighting city So you know, I don't want |
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| 157:51 | go around saying our tps thanks because gets you in some trouble. Um |
|
| 157:59 | then here is traditional versus differential RTP this beautiful data over the northern territories |
|
| 158:06 | Australia and then here's their pseudo I mean, I just think about |
|
| 158:13 | . Look look at this way this tells me this is a long |
|
| 158:18 | , like a deep source. But gravity is gonna say that this is |
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| 158:23 | big honk and density thing down That's not it at all. These |
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| 158:29 | these, that's why I think that gravity is wrong because people that are |
|
| 158:32 | it, they're saying that these these highs and lows ours well, |
|
| 158:39 | necessary structural, but what are you do? I mean, gravity is |
|
| 158:43 | of gravity is intuitive, right? yeah, it's never mind. But |
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| 158:51 | don't I don't think it works very . Okay. And then we looked |
|
| 158:56 | some notes about remnants and and uh Connor's burger In different ways to Clark's |
|
| 159:05 | with 11 reviews. He reviews 11 on estimating remnants from magnetic data and |
|
| 159:14 | this pseudo gravity. Um and then this pseudo gravity study of some rift |
|
| 159:24 | beneath beneath the ice and the transit And in the I'm sorry the in |
|
| 159:32 | Antarctica and they have a couple of have a crisis action here with this |
|
| 159:39 | their one interpretation and then here's their model of their of their backyard. |
|
| 159:45 | I thought well this is their final down here. But yeah and so |
|
| 159:49 | was the end end of that. you're welcome to ask me any questions |
|
| 159:57 | the week if you want. I care. We went through all this |
|
| 160:00 | um and I think um I think you kind of look at the same |
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| 160:06 | of compare, you can flip through yourself if you want and you can |
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| 160:11 | what the different enhancements due to the , both with regard to Magnetics as |
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| 160:18 | as to gravity. You kind of a good sense for Yeah for how |
|
| 160:25 | data are our enhanced and how how data can kind of kind of wreck |
|
| 160:33 | day. So is there anything else you want me to any I mean |
|
| 160:43 | not really, I feel pretty good it. So. Okay well then |
|
| 160:57 | sure to you know ask you any if you if you want. And |
|
| 161:04 | got in here, you wanted me send in that one paper, write |
|
| 161:06 | paper on what was that Schmidt right the you on the dynamo, |
|
| 161:21 | the dynamo right. Yeah, And I'm made some notes too and |
|
| 161:28 | go through these slides and I'll the with the ones from yesterday and any |
|
| 161:35 | I make, I'll highlight them then export them so that you can pay |
|
| 161:40 | and you'll see where there's a highlight see what what I change. But |
|
| 161:45 | this except except for this, you , I didn't make that. But |
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| 161:49 | text that's highlighted that stuff that I I changed so that I updated. |
|
| 161:57 | trying to get that done. Hopefully or tomorrow. No, it's not |
|
| 162:04 | be that much, but it's still to have corrected. Okay. Is |
|
| 162:12 | other class still going you take? , they're going, gosh, it's |
|
| 162:25 | hard because I'm by myself. So no like there's nobody to feed |
|
| 162:30 | I mean usually, you know two people I can do a lot |
|
| 162:35 | . I get pretty close to the with two people because you know, |
|
| 162:38 | they're interrupting me. They're you they're interrupting me more than you |
|
| 162:42 | So I guess I can just blame for this. Huh? I'll take |
|
| 162:47 | blame. Yeah. Well, all then, um if I'm please look |
|
| 162:57 | everything, make sure you understand because hate to you understand because I just |
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| 163:01 | through it too fast. But if understand it because I've taught this stuff |
|
| 163:07 | and I don't finish, you so early. No, I'll definitely |
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| 163:14 | through everything. I mean there there's that's confusing to me if that makes |
|
| 163:19 | . So actually we're actually not finishing we're not taking we're just taking a |
|
| 163:26 | short break. Take a short I mean, we almost we probably |
|
| 163:30 | an hour off easy. Hmm. didn't think about that, so. |
|
| 163:42 | right, then. Well, I guess I'll see you friday. |
|
| 163:45 | . See you friday |
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