WEBVTT 00:00:11.020 --> 00:00:16.899 Lost in the fog of a cosmic storm Floating on 00:00:16.899 --> 00:00:21.140 whimsical wavelengths is the norm Dancing through 00:00:21.140 --> 00:00:30.529 the stars, chasing spectrums of light Welcome 00:00:30.529 --> 00:00:33.530 back to Whimsical Wavelengths. This time we're 00:00:33.530 --> 00:00:36.170 digging deep into the bedrock of discovery. This 00:00:36.170 --> 00:00:38.789 episode is where geology meets algorithms, and 00:00:38.789 --> 00:00:41.250 the motherlode is just a model away. We're using 00:00:41.250 --> 00:00:43.310 computers to crunch the crust, letting machine 00:00:43.310 --> 00:00:45.350 learning mine the patterns hidden in the data. 00:00:46.170 --> 00:00:49.310 This episode is loaded with knowledge that's 00:00:49.310 --> 00:00:52.149 anything but artificial, even though the intelligence 00:00:52.149 --> 00:00:57.009 is... And the puns are terrible. So machine learning... 00:00:57.179 --> 00:00:59.179 The topic of this episode is a bit outside of 00:00:59.179 --> 00:01:01.460 my wheelhouse, so I have a guest, and no, not 00:01:01.460 --> 00:01:04.459 just any guest, a co -worker from my alter ego. 00:01:04.680 --> 00:01:07.260 Yes, so far we've had a couple of guests that 00:01:07.260 --> 00:01:09.560 use scientific results as the basis for their 00:01:09.560 --> 00:01:12.799 career outside of academic institutions. Let's 00:01:12.799 --> 00:01:16.040 add one more. Before I get further into the introduction, 00:01:16.260 --> 00:01:17.980 many months ago I had asked for someone from 00:01:17.980 --> 00:01:20.579 my company I work for, Computational Geoscience, 00:01:20.680 --> 00:01:23.420 to come on the podcast and talk about machine 00:01:23.420 --> 00:01:25.980 learning and its applications in mineral exploration. 00:01:26.780 --> 00:01:29.299 It got delayed due to getting some results and 00:01:29.299 --> 00:01:32.599 a shuffle of personnel and more normal real life 00:01:32.599 --> 00:01:35.560 stuff. So this conversation is months in the 00:01:35.560 --> 00:01:39.200 making. It's also all about science. Lots of 00:01:39.200 --> 00:01:41.560 science takes place within the corporate world. 00:01:41.640 --> 00:01:44.019 This here is a chance to look at one aspect. 00:01:44.299 --> 00:01:46.920 It isn't all about laboratories and classrooms. 00:01:47.099 --> 00:01:49.879 It's also about boardrooms. There's lots of ways 00:01:49.879 --> 00:01:54.719 to have a career in science. Now, let's set it 00:01:54.719 --> 00:01:57.010 up. We've been mining minerals from the ground 00:01:57.010 --> 00:01:59.569 for thousands of years. All the deposits close 00:01:59.569 --> 00:02:01.730 to the surface and near people have been mined. 00:02:01.730 --> 00:02:05.969 They're gone. So the deposits we have left are 00:02:05.969 --> 00:02:10.650 either in remote places or many hundreds of meters 00:02:10.650 --> 00:02:13.509 under the ground. That means they're harder to 00:02:13.509 --> 00:02:16.770 find and harder to extract those shiny resources 00:02:16.770 --> 00:02:20.689 needed for our modern world. Today we also have 00:02:20.689 --> 00:02:23.870 a lot of data. satellite imagery topography lidar 00:02:23.870 --> 00:02:26.370 geophysical surveys like gravity see episode 00:02:26.370 --> 00:02:29.490 12 for an explanation on boogie gravity or magnetics 00:02:29.490 --> 00:02:32.669 there's so much more you'll see that sites that 00:02:32.669 --> 00:02:35.430 have a lot of data typically also have a lot 00:02:35.430 --> 00:02:37.870 of drilling this is where they've cored into 00:02:37.870 --> 00:02:41.389 the earth to take samples deep within i think 00:02:41.389 --> 00:02:43.569 this is a good detour drilling especially when 00:02:43.569 --> 00:02:46.210 the area is not near a road it's it's quite an 00:02:46.210 --> 00:02:47.979 endeavor The drill rig is something that needs 00:02:47.979 --> 00:02:51.840 to be moved by heavy machinery or a large truck. 00:02:52.139 --> 00:02:54.840 Flying it into sight is not simple in many areas. 00:02:55.280 --> 00:02:57.719 They take a lot of fuel, a crew of at least four 00:02:57.719 --> 00:03:01.439 people to run 24 -7. Each hole, after all is 00:03:01.439 --> 00:03:04.780 said and done, costs well over $100 ,000. And 00:03:04.780 --> 00:03:07.520 what do you get? Cylindrical sections of rock 00:03:07.520 --> 00:03:11.719 for the entire drill hole. Being approximately 00:03:11.719 --> 00:03:13.900 six centimeters in diameter, the information 00:03:13.900 --> 00:03:18.159 brought up is expensive. It's the only way to 00:03:18.159 --> 00:03:20.439 directly observe what we infer from the surface. 00:03:20.620 --> 00:03:23.699 Is there mineralization there? Will it be there 00:03:23.699 --> 00:03:28.159 next? Mine. A lot is done with that core of rock. 00:03:28.520 --> 00:03:31.319 Geochemistry, rock types, structures, like fractures, 00:03:31.319 --> 00:03:34.060 are all written down and described. The point 00:03:34.060 --> 00:03:37.699 is you don't want to miss with the drill. Drilling 00:03:37.699 --> 00:03:41.319 is expensive. Whatever increases your success 00:03:41.319 --> 00:03:45.039 rate and costs less than drilling is useful. 00:03:46.370 --> 00:03:50.490 Enter mineral prospectivity. Geophysics, drilling, 00:03:50.610 --> 00:03:54.229 remote sensing. With all that data, it is still 00:03:54.229 --> 00:03:58.490 hard to find the shiny stuff. And as said, drilling 00:03:58.490 --> 00:04:01.889 is expensive. To help combat this, we turn to 00:04:01.889 --> 00:04:03.830 machine learning and artificial intelligence 00:04:03.830 --> 00:04:06.189 to find patterns in the data humans may miss. 00:04:06.370 --> 00:04:10.069 To get enlightenment on this, a data scientist 00:04:10.069 --> 00:04:13.210 and my co -worker from the best place to work 00:04:13.210 --> 00:04:16.800 on Earth. And no. I'm not just saying that. He 00:04:16.800 --> 00:04:18.899 joins the show. Here with us today is a data 00:04:18.899 --> 00:04:21.860 scientist with Computational Geosciences Incorporated. 00:04:21.959 --> 00:04:24.300 He got his Bachelor's of Science from the University 00:04:24.300 --> 00:04:26.500 of Leeds, a Master's from Western University, 00:04:26.720 --> 00:04:29.160 and then transitioned to industry using his degrees 00:04:29.160 --> 00:04:31.660 in geophysics and continues to use science to 00:04:31.660 --> 00:04:34.759 solve problems. Please welcome Frederick Jackson 00:04:34.759 --> 00:04:38.000 to Whimsical Wavelengths. Hi, Jeff. Thank you 00:04:38.000 --> 00:04:40.620 for having me. I'm a big fan. Oh, that's nice. 00:04:41.069 --> 00:04:44.629 And no, I didn't pay him to say that. For those 00:04:44.629 --> 00:04:46.910 that are longtime listeners, the reason why I 00:04:46.910 --> 00:04:48.610 made that joke is because the listener question 00:04:48.610 --> 00:04:51.250 at the end of episode 11 about the Spinosaurus 00:04:51.250 --> 00:04:54.629 controversies actually came from Frederick here. 00:04:54.850 --> 00:04:58.129 A trick up my sleeve to make sure at least one 00:04:58.129 --> 00:05:02.449 more person listened to the episode. But more 00:05:02.449 --> 00:05:04.509 broadly, how does your interest in dinosaurs 00:05:04.509 --> 00:05:07.189 and other science avenues fit into your path 00:05:07.189 --> 00:05:10.110 to where you are today as a data scientist? Yeah. 00:05:10.170 --> 00:05:14.110 And by the way, I follow paleontology quite closely. 00:05:14.170 --> 00:05:16.279 And there was actually... I don't know when you'll 00:05:16.279 --> 00:05:18.220 release this episode, but at the time of recording, 00:05:18.360 --> 00:05:20.720 there was a paper released yesterday, I believe, 00:05:20.860 --> 00:05:23.959 on describing a new Spinosaurus species, which 00:05:23.959 --> 00:05:26.379 the name I forget, but it's quite a similar species 00:05:26.379 --> 00:05:29.180 to Spinosaurus aegypticus, which you would have 00:05:29.180 --> 00:05:31.420 discussed, but it has this really cool head crest 00:05:31.420 --> 00:05:34.699 on the top of its head, probably for display 00:05:34.699 --> 00:05:38.500 purposes. It's a very interesting new discovery. 00:05:38.680 --> 00:05:41.519 So that's something your listeners might be... 00:05:42.009 --> 00:05:44.290 be interested to hear yeah to answer your question 00:05:44.290 --> 00:05:47.850 i think you know when i was a kid i thought dinosaurs 00:05:47.850 --> 00:05:51.430 were big and impressive and cool and as an adult 00:05:51.430 --> 00:05:54.730 uh i i actually think what makes them so interesting 00:05:54.730 --> 00:05:57.709 is the kind of mystery involved in understanding 00:05:57.709 --> 00:05:59.990 them because we you know we can't go back in 00:05:59.990 --> 00:06:02.509 time to see what they look like we we can't clone 00:06:02.509 --> 00:06:04.990 them and bring them back to life like uh in the 00:06:04.990 --> 00:06:07.069 movies so it's instead there's been these kind 00:06:07.069 --> 00:06:09.329 of centuries of detective work where you're kind 00:06:09.329 --> 00:06:11.990 of We're only just starting to scratch the surface 00:06:11.990 --> 00:06:14.209 of what they actually looked like and behaved 00:06:14.209 --> 00:06:17.129 like. And I think there are a lot of parallels 00:06:17.129 --> 00:06:21.189 between that and what we do at CGI. You know, 00:06:21.230 --> 00:06:24.410 we're doing geological and geophysical characterization. 00:06:24.529 --> 00:06:26.990 We're building methods that uncover the mysteries 00:06:26.990 --> 00:06:29.949 of the earth in the same way that paleontology 00:06:29.949 --> 00:06:32.589 uncovers the mysteries of ancient life. And I 00:06:32.589 --> 00:06:35.769 think both kind of, they require a certain imagination 00:06:35.769 --> 00:06:40.069 combined with evidence, like real evidence. So 00:06:40.069 --> 00:06:43.009 what you're saying is that your job today kind 00:06:43.009 --> 00:06:45.750 of marries some of the great things that you've 00:06:45.750 --> 00:06:48.149 always wanted to study, but provides a practical 00:06:48.149 --> 00:06:50.290 aspect to it moving forward, kind of like the 00:06:50.290 --> 00:06:52.550 industry and the like. Yeah, yeah, exactly. I 00:06:52.550 --> 00:06:55.110 think there's a common thread of, you know, I 00:06:55.110 --> 00:06:56.970 think all scientists have the kind of thirst 00:06:56.970 --> 00:07:00.470 for discovery in a way. Yeah, and that's like 00:07:00.470 --> 00:07:03.050 a perfect segue to the next question, because 00:07:03.050 --> 00:07:05.209 before we... kind of pivot towards the technical 00:07:05.209 --> 00:07:08.670 part the process of conducting science is just 00:07:08.670 --> 00:07:10.610 as important as the results sometimes and that's 00:07:10.610 --> 00:07:13.170 what we're talking about so here on whimsical 00:07:13.170 --> 00:07:15.029 wavelengths we've covered papers that have come 00:07:15.029 --> 00:07:17.850 from universities and museums mostly as far as 00:07:17.850 --> 00:07:20.670 like a direct paper from industry like we've 00:07:20.670 --> 00:07:22.689 had some industry types on but they didn't bring 00:07:22.689 --> 00:07:25.790 a paper they wrote for industry it was usually 00:07:25.790 --> 00:07:29.050 from it was either a topic or from their phd 00:07:29.050 --> 00:07:31.389 thesis or something like that and that's not 00:07:31.389 --> 00:07:34.850 a problem but This is pretty sure the first one 00:07:34.850 --> 00:07:37.410 directly from industry. Peer review is the same, 00:07:37.449 --> 00:07:39.769 though. But there are some differences. For us, 00:07:39.790 --> 00:07:42.490 because I'm also employed at Computational Geosciences, 00:07:42.490 --> 00:07:44.290 what's the biggest difference between industry 00:07:44.290 --> 00:07:48.110 science and what you would do just strictly for 00:07:48.110 --> 00:07:50.629 academics? Yeah, I've thought about this a lot, 00:07:50.670 --> 00:07:53.170 actually, because I obviously had a brief period 00:07:53.170 --> 00:07:56.009 in academia and published some papers and then 00:07:56.009 --> 00:07:58.379 took some... time off from the research side 00:07:58.379 --> 00:08:00.660 and now i'm kind of back in the area of publishing 00:08:00.660 --> 00:08:04.100 again i think the biggest difference is kind 00:08:04.100 --> 00:08:06.420 of the the consequence of actually being wrong 00:08:06.420 --> 00:08:09.480 with your results i think in academia if it doesn't 00:08:09.480 --> 00:08:12.920 uh quite work then you you might get your null 00:08:12.920 --> 00:08:15.079 results or whatever but and have to rethink your 00:08:15.079 --> 00:08:18.439 your project but um you know life goes on whereas 00:08:18.439 --> 00:08:21.439 uh if we if we tell a client to dig somewhere 00:08:21.439 --> 00:08:23.420 and it there's nothing there then that's a very 00:08:23.870 --> 00:08:27.370 expensive mistake for a client. So you're balancing 00:08:27.370 --> 00:08:31.850 this scientific discovery process using the science 00:08:31.850 --> 00:08:34.629 to find things with the kind of risk management 00:08:34.629 --> 00:08:37.210 side of things, like generating models, which 00:08:37.210 --> 00:08:39.830 are maybe slightly more cautious in terms of 00:08:39.830 --> 00:08:43.190 their predictive capabilities than otherwise. 00:08:43.490 --> 00:08:46.850 So I think it's the risk management side of things 00:08:46.850 --> 00:08:48.789 that's a bit different. Yeah. One of the other 00:08:48.789 --> 00:08:51.500 things I want to kind of poke at here is Peer 00:08:51.500 --> 00:08:53.539 review is the same, so once something gets through 00:08:53.539 --> 00:08:55.500 peer review, it doesn't really matter where it 00:08:55.500 --> 00:08:57.759 came from too, too much. Again, it's always one 00:08:57.759 --> 00:09:00.200 paper, so if it's industry, you should probably 00:09:00.200 --> 00:09:02.500 see it in an academic journal from an academic 00:09:02.500 --> 00:09:04.580 point of view eventually. You can't always just 00:09:04.580 --> 00:09:07.039 do one, but there's also a different emphasis 00:09:07.039 --> 00:09:09.419 on publishing in industry, isn't there? Like, 00:09:09.419 --> 00:09:12.379 you're not just in the pursuit of knowledge, 00:09:12.480 --> 00:09:16.059 you only publish kind of what you want to, right? 00:09:16.669 --> 00:09:18.669 Yeah, there's always like a financial incentive 00:09:18.669 --> 00:09:21.669 to it, I feel like. And in fact, a lot of people 00:09:21.669 --> 00:09:24.470 don't publish, right? Like a lot of companies 00:09:24.470 --> 00:09:26.990 will keep their trade secrets because that's 00:09:26.990 --> 00:09:30.850 their moneymaker. And yeah, I think I disagree 00:09:30.850 --> 00:09:33.610 with that. I think by publishing and discussing 00:09:33.610 --> 00:09:35.950 what we do at conferences and things, I think 00:09:35.950 --> 00:09:39.490 we're showing people that what we do is solid 00:09:39.490 --> 00:09:42.690 and backed by science and open to public scrutiny. 00:09:43.049 --> 00:09:46.299 I think that kind of... transparency builds a 00:09:46.299 --> 00:09:50.419 certain trust in what we do and I think if we 00:09:50.419 --> 00:09:54.960 can prove what we do in areas like this paper 00:09:54.960 --> 00:09:58.519 we're doing, we're studying some areas in Australia, 00:09:58.740 --> 00:10:00.460 I think if we can prove that it works there then 00:10:00.460 --> 00:10:04.320 people trust it when we suggest using it in other 00:10:04.320 --> 00:10:07.639 areas where people haven't looked before. All 00:10:07.639 --> 00:10:09.559 right, so the paper we're actually about to dive 00:10:09.559 --> 00:10:11.879 into with this Australia data is formulating 00:10:11.879 --> 00:10:15.899 gold prospectivity mapping as a constrained learning 00:10:15.899 --> 00:10:18.059 problem. It was submitted and accepted to the 00:10:18.059 --> 00:10:22.019 Society of Exploration Geophysics for a meeting 00:10:22.019 --> 00:10:24.419 last year for the Applied Geoscience and Energy. 00:10:25.799 --> 00:10:28.899 Is there anything that you need to add to from 00:10:28.899 --> 00:10:32.159 the preamble that was part of the intro, or what 00:10:32.159 --> 00:10:35.529 problem is this trying to address? By the way, 00:10:35.529 --> 00:10:38.110 quick update. It should be published this month. 00:10:38.250 --> 00:10:39.549 Again, I'm not sure when you're releasing this, 00:10:39.629 --> 00:10:41.730 but it should be published in February in the 00:10:41.730 --> 00:10:46.049 SEG library. So it should be accessible by the 00:10:46.049 --> 00:10:47.929 time this comes out, I would imagine. But yes, 00:10:48.070 --> 00:10:51.490 so no, I think you did a good job. I think the 00:10:51.490 --> 00:10:55.490 motivation really is how complicated mineral 00:10:55.490 --> 00:10:59.250 systems are. They can occur on these tiny little 00:10:59.250 --> 00:11:01.309 centimeter length scales, and they're formed 00:11:01.309 --> 00:11:03.730 by incredibly complex and poorly understood. 00:11:04.279 --> 00:11:08.519 geological causation. And traditional exploration, 00:11:08.659 --> 00:11:10.759 I think, is very good at filling in the gaps 00:11:10.759 --> 00:11:14.299 between your knowns. But it's the extrapolating 00:11:14.299 --> 00:11:17.139 which is the difficult part and the thing that 00:11:17.139 --> 00:11:22.039 AI prospectivity is attempting to approach. What 00:11:22.039 --> 00:11:24.360 we're trying to do is to build a model which 00:11:24.360 --> 00:11:27.620 can do that, can extrapolate from a well -known 00:11:27.620 --> 00:11:29.500 area into an area that it doesn't know about 00:11:29.500 --> 00:11:32.529 at all, whilst also... Managing that risk, again, 00:11:32.590 --> 00:11:34.809 it comes back to managing risk, managing the 00:11:34.809 --> 00:11:37.690 risk of the model hallucinating or getting confused 00:11:37.690 --> 00:11:40.529 when we move away from the safe areas, I guess. 00:11:41.090 --> 00:11:43.730 Okay, so let's expand some of those ideas out 00:11:43.730 --> 00:11:46.250 right here, I guess. Prospectivity and machine 00:11:46.250 --> 00:11:48.509 learning is essentially, it's the machine learning 00:11:48.509 --> 00:11:50.149 side of it that you're talking about, the networking. 00:11:50.450 --> 00:11:53.409 So the average person listening to this has heard 00:11:53.409 --> 00:11:56.850 of large language models like ChatGPT and that 00:11:56.850 --> 00:11:59.009 they're trained by scraping art and music and 00:11:59.009 --> 00:12:02.019 writing from the internet. in an effort to learn 00:12:02.019 --> 00:12:05.200 to respond more human -like and give information 00:12:05.200 --> 00:12:08.480 and answers. So leaving the pitfalls and issues 00:12:08.480 --> 00:12:12.220 with that process aside for now, so how does 00:12:12.220 --> 00:12:14.940 that work in principle here? Because that's what 00:12:14.940 --> 00:12:16.980 you're talking about, being able to extrapolate 00:12:16.980 --> 00:12:19.720 from a well -known area to a less known area. 00:12:19.799 --> 00:12:24.019 I'm assuming you're having to train on data very 00:12:24.019 --> 00:12:28.600 similarly to LLMs. How is it different and how 00:12:28.600 --> 00:12:31.899 is that kind of all taking place? Yeah, the concept 00:12:31.899 --> 00:12:34.840 is very similar. So as you say, ChatGPT has learned 00:12:34.840 --> 00:12:39.179 how humans communicate via its training data, 00:12:39.340 --> 00:12:41.720 which is the internet, and it's able to extrapolate 00:12:41.720 --> 00:12:44.799 its responses to things it hasn't heard before. 00:12:45.399 --> 00:12:49.200 And what we're doing is we're building an algorithm 00:12:49.200 --> 00:12:52.259 which has, in principle, if we've done our job, 00:12:52.360 --> 00:12:55.759 it's learned how mineral systems form via the... 00:12:56.059 --> 00:12:59.000 geological training data, and it's extrapolating 00:12:59.000 --> 00:13:02.299 those, I guess, like formation mechanisms to 00:13:02.299 --> 00:13:04.820 areas that it hasn't seen before in the same 00:13:04.820 --> 00:13:07.700 way ChatGPT has. So that's the similarity. I 00:13:07.700 --> 00:13:10.620 think the difference is the amount of data. I 00:13:10.620 --> 00:13:12.700 think ChatGPT obviously had the whole internet 00:13:12.700 --> 00:13:15.159 to scrape from. We don't quite have that privilege, 00:13:15.320 --> 00:13:19.500 so ChatGPT achieves. like uh insanely creepy 00:13:19.500 --> 00:13:21.879 level of human i don't know if you ever like 00:13:21.879 --> 00:13:23.460 speak to it you ever do like a back and forth 00:13:23.460 --> 00:13:25.600 conversation with it it's it's it's creepy how 00:13:25.600 --> 00:13:29.100 yeah and full disclosure i often give it podcast 00:13:29.100 --> 00:13:32.120 stuff and ask it to like critique my stuff and 00:13:32.120 --> 00:13:34.059 sometimes the stuff it gives me back you're like 00:13:34.059 --> 00:13:39.110 oh yeah Yeah, we don't quite have that level 00:13:39.110 --> 00:13:40.870 of training data. So again, it's back to that 00:13:40.870 --> 00:13:43.649 risk management thing. It's about how to make 00:13:43.649 --> 00:13:46.889 it work with the limited data that we have in 00:13:46.889 --> 00:13:49.570 the geological world. Because, you know, drilling 00:13:49.570 --> 00:13:52.389 is expensive in geological exploration. Geochemical 00:13:52.389 --> 00:13:56.149 sampling is often quite sparse. So it's about 00:13:56.149 --> 00:13:58.950 all those kind of limitations. Yeah, so maybe 00:13:58.950 --> 00:14:01.110 we'll take a quick aside there. Do you happen 00:14:01.110 --> 00:14:02.830 to know, roughly speaking, how much a drill hole 00:14:02.830 --> 00:14:06.190 costs? I actually have no idea, no. Oh, okay. 00:14:06.269 --> 00:14:09.029 Well, I guess for the listener, it's usually 00:14:09.029 --> 00:14:10.750 in the many hundreds of thousands of dollars, 00:14:10.850 --> 00:14:12.909 depending on how many you're going to get done. 00:14:13.009 --> 00:14:14.529 If you're just going to go do one drill hole, 00:14:14.629 --> 00:14:16.450 it's like a million dollars because you have 00:14:16.450 --> 00:14:19.830 to pay to get everybody on site. But then it's 00:14:19.830 --> 00:14:22.289 usually somewhere between $200 ,000 to $300 ,000 00:14:22.289 --> 00:14:24.820 per drill hole. I guess the depth as well depends 00:14:24.820 --> 00:14:26.500 on the depth. It depends on the depth. It depends 00:14:26.500 --> 00:14:27.860 on a lot of things. So if you're going to go 00:14:27.860 --> 00:14:29.840 really deep, then it's almost a million per drill 00:14:29.840 --> 00:14:32.100 hole. Or if you're going to do geothermal because 00:14:32.100 --> 00:14:34.340 of the size of the drill hole, it has to be, 00:14:34.379 --> 00:14:37.360 it'll be more expensive. But the point is being 00:14:37.360 --> 00:14:40.039 able to de -risk those drill holes is really 00:14:40.039 --> 00:14:44.480 important for the industry. So as you were saying, 00:14:44.580 --> 00:14:47.940 you don't have the same amounts of data as a 00:14:47.940 --> 00:14:49.840 lot of these LLMs because you're dealing with 00:14:49.840 --> 00:14:52.879 an area which we only can, We only have what 00:14:52.879 --> 00:14:55.360 information we have is what we can measure. So 00:14:55.360 --> 00:14:59.159 the paper uses data that is either already public 00:14:59.159 --> 00:15:01.360 or you've received some kind of special permission 00:15:01.360 --> 00:15:04.200 to be able to publish it, right? So what is that 00:15:04.200 --> 00:15:06.840 data that you're using to train in this paper, 00:15:06.860 --> 00:15:10.059 which is on Australia, and build that geological 00:15:10.059 --> 00:15:13.679 equivalent to ChatGPT? Yeah, so we're focusing 00:15:13.679 --> 00:15:16.600 on Western Australia and Australia is very good 00:15:16.600 --> 00:15:20.899 at releasing their data for public use. There's 00:15:20.899 --> 00:15:25.259 a website called Geoscience Australia that merges 00:15:25.259 --> 00:15:27.919 and makes available a lot of exploration data 00:15:27.919 --> 00:15:30.299 and they have essentially geophysics for all 00:15:30.299 --> 00:15:33.799 of Western Australia, which is absolutely ideal 00:15:33.799 --> 00:15:39.000 for a test project like this. So we went on Geoscience 00:15:39.000 --> 00:15:41.240 Australia, we assembled what we call a data cube, 00:15:41.379 --> 00:15:44.039 which is just a fancy name for a big folder of 00:15:44.039 --> 00:15:46.919 data. But we assembled, I think, something like 00:15:46.919 --> 00:15:49.740 15 different data layers of public data from 00:15:49.740 --> 00:15:52.759 the Yilgarn Craton, which is an area of Western 00:15:52.759 --> 00:15:55.200 Australia. And that includes potential fields 00:15:55.200 --> 00:15:58.379 data, which is your... magnetics and gravity 00:15:58.379 --> 00:16:02.179 data, the images into the subsurface. We have 00:16:02.179 --> 00:16:05.120 radiometrics, we have satellite imagery, and 00:16:05.120 --> 00:16:08.200 then we have the labels, which are geochemical 00:16:08.200 --> 00:16:10.940 samples and potentially the most important part 00:16:10.940 --> 00:16:13.840 of the whole puzzle. And the idea is to build 00:16:13.840 --> 00:16:17.639 up an analog to doing this on a more kind of 00:16:17.639 --> 00:16:21.240 brownfield level, I guess, like the kind of data 00:16:21.240 --> 00:16:23.700 that a client might potentially have at a kind 00:16:23.700 --> 00:16:26.679 of exploration target. Okay. only because I know 00:16:26.679 --> 00:16:28.940 the words, and I'm going to try to expand them 00:16:28.940 --> 00:16:30.980 out here a little bit. When we deal with, I guess, 00:16:31.059 --> 00:16:33.460 potential fields, we're talking about someone's 00:16:33.460 --> 00:16:35.940 gone around and measured the Earth's magnetic 00:16:35.940 --> 00:16:38.639 field or gravitational field over a wide area, 00:16:38.740 --> 00:16:41.320 and you're taking that map and derivatives of 00:16:41.320 --> 00:16:44.679 that map as some of your data, right? Yeah, exactly. 00:16:45.450 --> 00:16:48.169 And for those that are not in the industry, brownfields 00:16:48.169 --> 00:16:51.830 means like you've had enough time to collect 00:16:51.830 --> 00:16:54.149 a lot of different data. You know that there's 00:16:54.149 --> 00:16:56.009 something under the ground and you're moving 00:16:56.009 --> 00:16:59.129 towards hopefully opening a mine. In a lot of 00:16:59.129 --> 00:17:01.429 cases when we're looking for minerals, we call 00:17:01.429 --> 00:17:03.809 it greenfields when we don't have a lot of data 00:17:03.809 --> 00:17:06.410 and you've maybe got one or two drill holes kind 00:17:06.410 --> 00:17:09.089 of idea. still trying to suss out whether or 00:17:09.089 --> 00:17:11.029 not there's anything of economic worth underneath 00:17:11.029 --> 00:17:12.970 the ground. Whereas brownfields, you're starting 00:17:12.970 --> 00:17:15.589 to move to words of mine. That doesn't mean you'll 00:17:15.589 --> 00:17:16.990 get there, but that's where you're moving to. 00:17:17.049 --> 00:17:19.930 So you have a lot of data, or at least a lot 00:17:19.930 --> 00:17:24.390 of data in the sense of geology and data sets 00:17:24.390 --> 00:17:27.369 that I guess you can use for prospectivity. That 00:17:27.369 --> 00:17:28.630 summed it up pretty good. Did I miss anything? 00:17:29.210 --> 00:17:33.109 No. Yeah, perfect. And sorry, I'm used to presenting 00:17:33.109 --> 00:17:35.230 this at conferences and things where the terminology 00:17:35.230 --> 00:17:37.559 is more known. Yeah, but you're also talking 00:17:37.559 --> 00:17:40.119 to me, a co -worker who is going to understand 00:17:40.119 --> 00:17:42.920 what you say kind of instantaneously instead 00:17:42.920 --> 00:17:45.720 of for everyone who's listening somewhere else. 00:17:45.859 --> 00:17:48.480 I mean, don't be afraid to get nerdy, my friend. 00:17:48.619 --> 00:17:51.299 I mean, we all love maps, or at least we do. 00:17:52.140 --> 00:17:54.400 So I guess the next part of the conversation 00:17:54.400 --> 00:17:56.339 comes back to the geochem that you were just 00:17:56.339 --> 00:17:58.759 saying. The idea of like, we've already talked 00:17:58.759 --> 00:18:01.259 about the different data layers. But the data 00:18:01.259 --> 00:18:04.240 layers isn't really what you're training on, 00:18:04.380 --> 00:18:06.819 right? That's just giving the information for 00:18:06.819 --> 00:18:11.059 it to make pattern recognition with geologic 00:18:11.059 --> 00:18:14.980 layers or geologic targets versus what LLMs do 00:18:14.980 --> 00:18:18.720 with words, right? So you need to give it something, 00:18:18.920 --> 00:18:22.460 and you call that the labels. Yeah, yeah. So 00:18:22.460 --> 00:18:25.599 in machine learning, you classify your data into 00:18:25.599 --> 00:18:30.569 features and labels. So features are... the kind 00:18:30.569 --> 00:18:33.970 of wide area extensive geophysics in this case 00:18:33.970 --> 00:18:36.549 or remote sensing and labels are the actual ground 00:18:36.549 --> 00:18:39.950 truth so the the concept of machine learning 00:18:39.950 --> 00:18:42.430 is that you're building this kind of causative 00:18:42.430 --> 00:18:45.789 link between your labels and your features so 00:18:45.789 --> 00:18:49.009 the the features are the observable thing which 00:18:49.009 --> 00:18:51.910 explain the existence of the labels so in this 00:18:51.910 --> 00:18:54.170 case the the ground truth from from the drilling 00:18:54.170 --> 00:18:57.390 is the labels and the geophysics is the thing 00:18:57.390 --> 00:19:00.250 which is kind of explaining the the reason for 00:19:00.250 --> 00:19:03.589 the labels being what they are. Okay. Just out 00:19:03.589 --> 00:19:06.710 of curiosity, there must be some like pitfalls 00:19:06.710 --> 00:19:09.009 to this type of approach as well, right? Like 00:19:09.009 --> 00:19:11.670 in my own head, I come up with the simple thing 00:19:11.670 --> 00:19:14.009 is that if you're using ground geochemistry, 00:19:14.089 --> 00:19:17.910 something could have changed for the lack of 00:19:17.910 --> 00:19:20.250 a, obviously we wouldn't use this necessarily, 00:19:20.329 --> 00:19:24.710 but glaciers or rivers or flash floods can move 00:19:24.710 --> 00:19:29.250 things downstream and modify what we see. Is 00:19:29.250 --> 00:19:32.009 that a concern here? Is it things that modify 00:19:32.009 --> 00:19:34.569 either humans or natural events that modify the 00:19:34.569 --> 00:19:37.369 subsurface? Is it a case of garbage in, garbage 00:19:37.369 --> 00:19:41.690 out? Yeah, absolutely. That's the big caveat 00:19:41.690 --> 00:19:43.970 of all AI is the garbage in, garbage out thing. 00:19:44.450 --> 00:19:47.430 And geochemical sampling is especially prone 00:19:47.430 --> 00:19:50.509 to that human or instrument error or transported 00:19:50.509 --> 00:19:54.470 samples, like you were saying. And when you're 00:19:54.470 --> 00:19:56.910 building that causative link I mentioned, then... 00:19:57.279 --> 00:19:58.960 that can obviously throw off your entire model. 00:19:59.180 --> 00:20:01.859 So yeah, garbage in, garbage out for sure. I 00:20:01.859 --> 00:20:03.339 mean, again, it comes back to that risk management 00:20:03.339 --> 00:20:06.279 thing, building these for real world applications. 00:20:06.380 --> 00:20:08.839 And that kind of prompted our approach that we 00:20:08.839 --> 00:20:11.180 present in this paper. Actually, there's a nice 00:20:11.180 --> 00:20:13.200 lead into that because we don't actually care 00:20:13.200 --> 00:20:16.460 in our approach about the exact granular noise 00:20:16.460 --> 00:20:19.400 of a geochemical sample. We're kind of more looking 00:20:19.400 --> 00:20:22.000 at the bigger picture, the proximity to the kind 00:20:22.000 --> 00:20:26.410 of high grade hits rather than the misses. So 00:20:26.410 --> 00:20:28.289 it's more resilient to that garbage because we're 00:20:28.289 --> 00:20:30.890 kind of building this sort of probability model 00:20:30.890 --> 00:20:32.750 based on the bigger picture rather than the kind 00:20:32.750 --> 00:20:35.970 of individual samples. OK, and that's what is 00:20:35.970 --> 00:20:38.569 new and novel with the paper. Is that specifically? 00:20:39.289 --> 00:20:41.970 Yes, exactly. I think we've kind of skipped over 00:20:41.970 --> 00:20:44.250 one part of this. So we've just kind of I've 00:20:44.250 --> 00:20:45.910 kind of waved a magic wand and said it's machine 00:20:45.910 --> 00:20:49.029 learning. We haven't really described what machine 00:20:49.029 --> 00:20:52.210 learning is. Yeah, well, machine learning is 00:20:52.210 --> 00:20:55.859 is kind of. building that kind of causative link 00:20:55.859 --> 00:20:57.940 that I mentioned between some kind of feature 00:20:57.940 --> 00:21:00.059 and some kind of label, and building a model 00:21:00.059 --> 00:21:02.319 that understands that and is then able to extrapolate 00:21:02.319 --> 00:21:06.099 it to kind of unknown areas where we don't necessarily 00:21:06.099 --> 00:21:09.420 have that understanding. Yeah, I was going to 00:21:09.420 --> 00:21:11.599 give a bit more into the details, really get 00:21:11.599 --> 00:21:14.259 nerdy here. Neural networks is more where I'm 00:21:14.259 --> 00:21:16.019 going because there's different types, right? 00:21:16.160 --> 00:21:19.220 Not all machine learning is the same, built the 00:21:19.220 --> 00:21:21.930 same way. yeah for sure so we're using a neural 00:21:21.930 --> 00:21:24.049 network in this paper and a neural network is 00:21:24.049 --> 00:21:26.450 based on the architecture of a human brain essentially 00:21:26.450 --> 00:21:30.609 so um it's has various kind of neural nodes and 00:21:30.609 --> 00:21:32.710 and you know connections between those nodes 00:21:32.710 --> 00:21:36.130 and is assembling a uh kind of unquantifiable 00:21:36.130 --> 00:21:39.930 knowledge of of those relationships uh in a in 00:21:39.930 --> 00:21:43.230 a different way to the way that like a regression 00:21:43.230 --> 00:21:45.150 is a machine learning method but that's learning 00:21:45.150 --> 00:21:47.309 a very like simple linear relationship between 00:21:47.309 --> 00:21:52.309 things this is a very complex, non -linear, like 00:21:52.309 --> 00:21:56.109 very hard to even sort of picture in your head 00:21:56.109 --> 00:21:58.210 really what a neural network looks like. It's 00:21:58.210 --> 00:22:02.650 kind of beyond visualization. And that kind of 00:22:02.650 --> 00:22:06.569 gets to how advanced these methods are. Complete 00:22:06.569 --> 00:22:09.210 aside, you said it's a lot like the brain. Do 00:22:09.210 --> 00:22:13.009 the two fields actually kind of intersect there 00:22:13.009 --> 00:22:16.269 a little bit? What do you mean? Are we programming 00:22:16.269 --> 00:22:18.690 human beings here? Are we actually taking what 00:22:18.690 --> 00:22:21.630 we understand of a brain and actually utilizing 00:22:21.630 --> 00:22:23.490 that information to create a neural network? 00:22:23.849 --> 00:22:26.509 Yeah, in a way, you can kind of look at it in 00:22:26.509 --> 00:22:29.410 that we're sort of creating an artificial geologist 00:22:29.410 --> 00:22:33.069 in a lot of ways. A geologist that is able to 00:22:33.069 --> 00:22:36.589 look at data and kind of use their own understanding 00:22:36.589 --> 00:22:38.950 of the connections between the data and then 00:22:38.950 --> 00:22:41.829 make predictions based on what it sees. Obviously, 00:22:41.829 --> 00:22:43.740 it's a bit more... complicated than that. And 00:22:43.740 --> 00:22:46.220 we don't want to replace our geologist friends. 00:22:46.420 --> 00:22:48.640 We're always going to need their inputs. And 00:22:48.640 --> 00:22:52.220 these things are being built as tools for geologists. 00:22:52.420 --> 00:22:56.640 But it is essentially a brain that we're creating 00:22:56.640 --> 00:22:59.859 here. Yeah, I guess it takes a geologist to understand 00:22:59.859 --> 00:23:02.880 the output. Yeah. You're never going to replace 00:23:02.880 --> 00:23:05.900 the geologist because they have to feed it. And 00:23:05.900 --> 00:23:07.839 then they have to understand the output. Yeah, 00:23:07.880 --> 00:23:10.470 absolutely. I guess that gets to the next part 00:23:10.470 --> 00:23:12.009 of the question. Like AI and machine learning, 00:23:12.089 --> 00:23:14.470 it's revolutionizing some aspects. In some cases, 00:23:14.529 --> 00:23:19.549 it's more, instead of revolutionizing something, 00:23:19.710 --> 00:23:24.369 it's like methods of marketing. I guess this 00:23:24.369 --> 00:23:26.329 happens every time there's a new technological 00:23:26.329 --> 00:23:29.329 disruptor that's out there. But this is peer 00:23:29.329 --> 00:23:31.630 -reviewed. It's solid science backing with real 00:23:31.630 --> 00:23:34.329 -world results. What's the strategy to break 00:23:34.329 --> 00:23:36.450 through the noise around AI and deliver real 00:23:36.450 --> 00:23:38.589 results and get people to believe in it? I mean, 00:23:38.609 --> 00:23:40.490 it's not really to believe in it, but to understand 00:23:40.490 --> 00:23:42.730 the results and how they can use it. There's 00:23:42.730 --> 00:23:46.930 just so much out there right now. I mean, I wish 00:23:46.930 --> 00:23:50.690 I knew the best strategy, Jeff. I think our strategy 00:23:50.690 --> 00:23:54.549 is to just kind of be as honest, like scientifically 00:23:54.549 --> 00:23:57.789 honest as possible about the capabilities of 00:23:57.789 --> 00:23:59.910 this and also the limitations. I think a lot 00:23:59.910 --> 00:24:03.049 of those kind of hype companies don't. discuss 00:24:03.049 --> 00:24:05.670 the limitations and that's what makes it snake 00:24:05.670 --> 00:24:08.769 oil in my belief. I think what we're doing by 00:24:08.769 --> 00:24:11.430 regularly publishing and talking about what we 00:24:11.430 --> 00:24:16.289 do is building that trust and acknowledging the 00:24:16.289 --> 00:24:20.490 inherent faults of all AI kind of allows us to 00:24:20.490 --> 00:24:22.650 actually work around those faults in a smart 00:24:22.650 --> 00:24:25.690 way by integrating as much input and knowledge 00:24:25.690 --> 00:24:29.309 from geologists and field experts to kind of 00:24:29.309 --> 00:24:32.069 build that tool rather than the sort of vague 00:24:32.069 --> 00:24:35.690 black box that I think has a lot of pitfalls 00:24:35.690 --> 00:24:38.609 in our industry. Okay, so let's pivot back to 00:24:38.609 --> 00:24:41.349 the results. So this is an evolving tool to find 00:24:41.349 --> 00:24:45.990 mineral deposits, but what did you find by going 00:24:45.990 --> 00:24:48.369 through this study? Was there anything new and 00:24:48.369 --> 00:24:51.529 surprising that we can discuss here? Yeah, I 00:24:51.529 --> 00:24:54.930 mean, there's nothing particularly revolutionary 00:24:54.930 --> 00:24:57.509 about what we did. It's more kind of how we framed 00:24:57.509 --> 00:25:02.079 the problem, I guess. In terms of where this 00:25:02.079 --> 00:25:04.019 will be going in future, I think the holy grail 00:25:04.019 --> 00:25:06.400 is to integrate that kind of 3D information. 00:25:06.599 --> 00:25:12.019 This is a very 2D problem. And I think we want 00:25:12.019 --> 00:25:14.380 to... There's one thing like predicting where 00:25:14.380 --> 00:25:17.380 to drill on a map, but actually like how deep 00:25:17.380 --> 00:25:19.960 to drill is kind of the holy grail in my opinion. 00:25:20.160 --> 00:25:24.319 So I think the kind of evolving steps are to 00:25:24.319 --> 00:25:27.299 look into hybrid approaches that kind of combine 00:25:27.299 --> 00:25:31.309 the advantages of... neural networks and AI with 00:25:31.309 --> 00:25:37.009 traditional geophysical inversions and 3D interpolations 00:25:37.009 --> 00:25:40.369 as well. And I think we hope to be able to talk 00:25:40.369 --> 00:25:42.849 about that in the near future. Yeah, I guess 00:25:42.849 --> 00:25:44.390 we didn't even touch on the fact that this is 00:25:44.390 --> 00:25:47.109 really providing a 2D map, right? Like the idea 00:25:47.109 --> 00:25:50.369 is you're providing a heat map or something like 00:25:50.369 --> 00:25:53.349 a heat map that you get warmer colors when you're 00:25:53.349 --> 00:25:55.410 closer to what you're looking for. In this case 00:25:55.410 --> 00:25:59.289 was gold, right? Yes, exactly. Yeah. So I guess 00:25:59.289 --> 00:26:02.170 the obvious question is, why can't we do it in 00:26:02.170 --> 00:26:04.990 3D? Like, what's the limitation? I think that 00:26:04.990 --> 00:26:08.309 there is a computational limitation. I think 00:26:08.309 --> 00:26:12.410 it's also just the kind of how you frame the 00:26:12.410 --> 00:26:14.490 problem. I think this is a very unconstrained 00:26:14.490 --> 00:26:17.289 kind of solution that we've developed. I think 00:26:17.289 --> 00:26:21.720 a 3D solution because it's in so much more. resolution 00:26:21.720 --> 00:26:23.779 and there's so much more information, I think 00:26:23.779 --> 00:26:26.180 you have to introduce a lot of constraints to 00:26:26.180 --> 00:26:28.940 that in a positive way. I think that's a good 00:26:28.940 --> 00:26:31.819 thing to produce models which are consistent 00:26:31.819 --> 00:26:37.259 with your actual 3D information. You just have 00:26:37.259 --> 00:26:39.140 to parameterize that a little bit differently 00:26:39.140 --> 00:26:43.559 and it's not something which is unachievable 00:26:43.559 --> 00:26:47.650 and it is something that we are working on. So 00:26:47.650 --> 00:26:51.089 you would say it's more a function of not having 00:26:51.089 --> 00:26:54.890 3D data? Like we have some, but again, being 00:26:54.890 --> 00:26:58.289 a geophysicist and we've covered inversions before, 00:26:58.509 --> 00:27:02.809 inversions are non -unique. So you can use them, 00:27:02.890 --> 00:27:05.769 but you can't use them for your labels, right? 00:27:06.549 --> 00:27:12.890 Yeah, exactly. An inversion is a subjective interpretation 00:27:12.890 --> 00:27:17.859 of objective data. And that's where the non -uniqueness 00:27:17.859 --> 00:27:20.759 comes from. And there's an infinite amount of 00:27:20.759 --> 00:27:26.960 models which satisfy a given data set. And we 00:27:26.960 --> 00:27:29.839 do things in Inversion to try and reduce the 00:27:29.839 --> 00:27:32.380 amount of models which do satisfy that data set. 00:27:32.500 --> 00:27:36.059 But the more you can constrain and limit that 00:27:36.059 --> 00:27:41.400 process, the better. So I guess more to the frame, 00:27:41.539 --> 00:27:44.119 I'm familiar with geophysics and Inversion models. 00:27:44.809 --> 00:27:47.210 And I guess I'm familiar with geologic models, 00:27:47.369 --> 00:27:49.069 but generally speaking, there isn't that many 00:27:49.069 --> 00:27:52.109 geologic models out there to be had that aren't 00:27:52.109 --> 00:27:54.150 similar to inversion models, that they're just 00:27:54.150 --> 00:27:56.170 interpolated between. We don't have a lot of 00:27:56.170 --> 00:27:58.589 labels. So is part of the reason why we can't 00:27:58.589 --> 00:28:02.089 have these 3D prospectivity more to the fact 00:28:02.089 --> 00:28:04.509 that we just don't have 3D data? Like, how do 00:28:04.509 --> 00:28:07.930 you bring a DEM into depth or things that you 00:28:07.930 --> 00:28:09.950 collect on the Earth's surface into something 00:28:09.950 --> 00:28:12.839 you can actually... The only thing I can think 00:28:12.839 --> 00:28:16.079 of we have is the drill holes. Yeah, and drill 00:28:16.079 --> 00:28:18.500 holes are obviously incredibly valuable that 00:28:18.500 --> 00:28:21.500 they're expensive, but that's an objective observation 00:28:21.500 --> 00:28:24.619 of what the lithology actually looks like. And 00:28:24.619 --> 00:28:27.920 you often get geophysical information with that 00:28:27.920 --> 00:28:30.660 too. So I think the real value comes from combining 00:28:30.660 --> 00:28:33.779 that knowledge of lithology, knowledge of downhole 00:28:33.779 --> 00:28:35.980 geophysics, and then combining that with surface 00:28:35.980 --> 00:28:39.400 geophysics. and kind of building something that 00:28:39.400 --> 00:28:43.359 satisfies all of those at the same time. And 00:28:43.359 --> 00:28:45.099 it's a slightly different problem to the one 00:28:45.099 --> 00:28:47.000 we tackled in this paper. This paper was more 00:28:47.000 --> 00:28:51.140 about a kind of rapid 2D kind of de -risking 00:28:51.140 --> 00:28:55.240 product. It's not supposed to be objective or 00:28:55.240 --> 00:28:59.059 an accurate model of the subsurface. It's supposed 00:28:59.059 --> 00:29:02.619 to be something for simple de -risking and for 00:29:02.619 --> 00:29:06.609 exploration targeting. I think what... The next 00:29:06.609 --> 00:29:10.750 step is this kind of 3D modeling, which builds 00:29:10.750 --> 00:29:13.509 a slightly more targetable model in terms of 00:29:13.509 --> 00:29:16.410 drilling. Yeah, and actually, there's a couple 00:29:16.410 --> 00:29:18.710 of ways I've also seen this used. You're probably 00:29:18.710 --> 00:29:21.349 aware of them as well. Like in British Columbia, 00:29:21.470 --> 00:29:25.589 they sent out a request for proposals from companies 00:29:25.589 --> 00:29:27.910 such as ours. We didn't take them up on this 00:29:27.910 --> 00:29:30.529 to figure out where places were perspective, 00:29:30.769 --> 00:29:32.829 so that way they could make decisions based on... 00:29:33.309 --> 00:29:36.730 parks and i guess first nations rights and stuff 00:29:36.730 --> 00:29:39.309 like that and where to start that initial conversations 00:29:39.309 --> 00:29:42.269 are there other examples where it's being used 00:29:42.269 --> 00:29:44.750 to inform policy instead of directly trying to 00:29:44.750 --> 00:29:47.410 find the next deposit no i mostly people are 00:29:47.410 --> 00:29:50.109 quite distrustful of this i feel like um it's 00:29:50.109 --> 00:29:52.990 that there's a few like national efforts to to 00:29:52.990 --> 00:29:56.089 do mineral prospectivity generally they're quite 00:29:56.089 --> 00:29:58.549 simplistic and i don't quite know the use cases 00:29:58.549 --> 00:30:02.440 for them i think I think generally the main driver 00:30:02.440 --> 00:30:05.819 is coming from industry, from mines and from 00:30:05.819 --> 00:30:08.480 that kind of brownfield, which means on mine 00:30:08.480 --> 00:30:11.859 site kind of targeting. So you're saying perhaps 00:30:11.859 --> 00:30:16.700 most of the real world case examples are locked 00:30:16.700 --> 00:30:19.140 behind data vaults that never get published. 00:30:19.819 --> 00:30:22.579 Exactly, yeah. What about other things that this 00:30:22.579 --> 00:30:25.259 same technology can be used for? Like, they can't 00:30:25.259 --> 00:30:27.140 be just for finding minerals. There's got to 00:30:27.140 --> 00:30:32.380 be water resources. identifying things on the 00:30:32.380 --> 00:30:35.720 Earth's surface? Yeah, we never reinvented the 00:30:35.720 --> 00:30:38.660 wheel with this technique. We produced a novel 00:30:38.660 --> 00:30:40.759 approach to it, but the actual technique is very 00:30:40.759 --> 00:30:43.839 grounded in decades of research. So this is a 00:30:43.839 --> 00:30:46.720 common thing in neuroscience and in medical sciences. 00:30:47.440 --> 00:30:50.579 It's the same technology that you would use to 00:30:50.579 --> 00:30:53.240 detect brain tumors, for example. You would train 00:30:53.240 --> 00:30:55.740 the model to detect the... the causative link 00:30:55.740 --> 00:31:00.559 between MRI images and brain tumors. And we actually 00:31:00.559 --> 00:31:04.339 started this study with a proof of concept using 00:31:04.339 --> 00:31:07.140 this malarial prevention data set, which was 00:31:07.140 --> 00:31:10.140 satellite imagery, or aerial imagery, sorry, 00:31:10.220 --> 00:31:15.240 of water tanks in Central America for the purpose 00:31:15.240 --> 00:31:18.579 of mapping the locations where mosquitoes might 00:31:18.579 --> 00:31:21.710 breed. And that was kind of our proof of concept 00:31:21.710 --> 00:31:25.069 was identifying water tanks in satellite, sorry, 00:31:25.690 --> 00:31:30.490 aerial data. This is a very kind of unknown technique. 00:31:30.549 --> 00:31:34.130 And the novel thing is just kind of how we framed 00:31:34.130 --> 00:31:38.390 that problem. So we just went from mineral prospectivity 00:31:38.390 --> 00:31:42.930 to medical imaging to finding where mosquitoes 00:31:42.930 --> 00:31:45.529 breed to help control malaria all in the same 00:31:45.529 --> 00:31:49.069 sentence. Yeah. Yeah, a bit of a roller coaster. 00:31:49.509 --> 00:31:52.829 No, but I mean, that just shows like how wide 00:31:52.829 --> 00:31:56.329 this data or this technique or general science 00:31:56.329 --> 00:31:58.509 behind the technique can be used for, right? 00:31:58.609 --> 00:32:00.650 Like this is going to have real world implications. 00:32:01.230 --> 00:32:05.069 Yeah, absolutely. And I think it will change 00:32:05.069 --> 00:32:08.309 the world of mineral exploration eventually. 00:32:08.490 --> 00:32:10.390 I don't know if it's quite there yet, but it's 00:32:10.390 --> 00:32:14.029 something that will only grow over time. So before 00:32:14.029 --> 00:32:17.119 we go to the traditional last question. All things 00:32:17.119 --> 00:32:18.359 being equal, is there anything else you want 00:32:18.359 --> 00:32:20.880 to share science -wise? A future trip, a paper, 00:32:21.019 --> 00:32:23.400 a documentary suggestion? I don't know, is there 00:32:23.400 --> 00:32:26.660 a new Spinosaurus documentary coming out? I don't 00:32:26.660 --> 00:32:29.180 know, that would be cool. I mentioned the Spinosaurus 00:32:29.180 --> 00:32:31.220 paper earlier. I recommend checking that out. 00:32:31.680 --> 00:32:35.599 My latest extracurricular obsession has been... 00:32:35.839 --> 00:32:39.160 something called bioacoustics which is uh studying 00:32:39.160 --> 00:32:42.180 the the vocalizations of marine life because 00:32:42.180 --> 00:32:44.519 i'm really into whale watching and so i've been 00:32:44.519 --> 00:32:46.660 doing a lot of research into that and i might 00:32:46.660 --> 00:32:48.819 be potentially getting involved in some research 00:32:48.819 --> 00:32:50.859 in bioacoustics in the future which would be 00:32:50.859 --> 00:32:53.420 very exciting but yeah i think that's a really 00:32:53.420 --> 00:32:56.470 interesting emerging field being able to track 00:32:56.470 --> 00:33:00.710 the location and movements of whales for conservation 00:33:00.710 --> 00:33:04.650 purposes and for notifying ships of when there's 00:33:04.650 --> 00:33:06.690 whales in the area and that kind of thing. Cool. 00:33:07.009 --> 00:33:09.490 Yeah, we had someone on to talk about birds at 00:33:09.490 --> 00:33:12.069 one point and being able to identify species, 00:33:12.470 --> 00:33:15.450 although not yet individuals, from recordings 00:33:15.450 --> 00:33:19.029 just out in the trees. So that's cool. Is that 00:33:19.029 --> 00:33:22.190 like a citizen science thing? Exactly, yeah. 00:33:22.190 --> 00:33:23.730 There's a couple of different citizen science. 00:33:24.509 --> 00:33:27.609 organizations that work on that and again it's 00:33:27.609 --> 00:33:30.009 using neural networks like we have in this study 00:33:30.009 --> 00:33:33.450 to identify what whale vocalizations look like 00:33:33.450 --> 00:33:35.549 and it requires that kind of building of the 00:33:35.549 --> 00:33:39.430 training data for the predictive models. Is there 00:33:39.430 --> 00:33:42.250 any group or anyone that might be listening in 00:33:42.250 --> 00:33:43.910 the Vancouver area that they should get in contact 00:33:43.910 --> 00:33:46.369 with if they want to join this citizen science 00:33:46.369 --> 00:33:49.829 outreach? Yeah, there's a really cool organization 00:33:49.829 --> 00:33:53.440 based in in Washington called Orca Sound. And 00:33:53.440 --> 00:33:55.880 they're the main citizen science people working 00:33:55.880 --> 00:33:58.420 on predictive models. And then there's a couple 00:33:58.420 --> 00:34:00.519 more. The rest are kind of tied to universities. 00:34:00.779 --> 00:34:04.559 So there's a research team at SFU called HALO. 00:34:04.900 --> 00:34:10.119 And then there's also some guys at UBC and UVic 00:34:10.119 --> 00:34:12.320 as well, all kind of working. They all kind of 00:34:12.320 --> 00:34:14.139 work together. It's all a very collaborative 00:34:14.139 --> 00:34:17.039 approach. So I think Orca HALO. The way science 00:34:17.039 --> 00:34:19.900 should be. Yes, exactly. I think Orca Hello is 00:34:19.900 --> 00:34:23.159 the main citizen science kind of approach. All 00:34:23.159 --> 00:34:26.260 right. Now for the traditional last infamous 00:34:26.260 --> 00:34:29.079 question. What is your favorite science joke? 00:34:29.539 --> 00:34:31.820 Yeah, I've got one for your longtime listeners 00:34:31.820 --> 00:34:37.260 that ties into the paper I mentioned earlier. 00:34:37.440 --> 00:34:42.300 So my joke is a Spinosaurus walks into a bar. 00:34:42.840 --> 00:34:47.079 The bartender asks him, why the long sail? Spinosaurus 00:34:47.079 --> 00:34:52.429 says, you know, I have absolutely no idea. The 00:34:52.429 --> 00:34:56.630 joke is that we have no idea what Spinosaurus... 00:34:56.630 --> 00:34:59.730 What it used to say. What it did. I got it now. 00:34:59.789 --> 00:35:02.429 Yeah, yeah. Takes a minute. Oh, boy. Normally, 00:35:02.469 --> 00:35:07.809 I get it right away, or I feign polite indifference. 00:35:08.550 --> 00:35:11.110 This time, I was like, no, no, I'm going to ask 00:35:11.110 --> 00:35:12.969 because I really need to know. Okay, well, thank 00:35:12.969 --> 00:35:15.199 you so much for being on. Yeah, thanks, Jeff. 00:35:15.300 --> 00:35:17.780 It was a pleasure to be on, and I'd love to come 00:35:17.780 --> 00:35:20.059 back sometime. Fantastic, but you'll need a new 00:35:20.059 --> 00:35:22.960 joke. Yes, one that works immediately this time. 00:35:23.179 --> 00:35:25.500 Well, that brings another episode to a close. 00:35:26.019 --> 00:35:28.739 Thanks for joining so far. Remember, send some 00:35:28.739 --> 00:35:31.440 feedback via the email in the show notes. Reach 00:35:31.440 --> 00:35:33.699 out on social media. Make sure you've subscribed 00:35:33.699 --> 00:35:35.820 to the show so you don't miss an episode. And 00:35:35.820 --> 00:35:39.000 give the gift of science. Tell a friend. And 00:35:39.000 --> 00:35:46.739 I'll see you in two weeks. so chaotic so misbehaved 00:35:46.739 --> 00:35:53.780 echoes of melodies remind us it's always colors 00:35:53.780 --> 00:36:01.559 weave stories painting the sky swaying to rhythms 00:36:01.559 --> 00:36:21.059 as the galaxies fly by Deep in our bones Through 00:36:21.059 --> 00:36:28.260 this melodic maze Our minds explore Change