lost in the fog of a cosmic storm floating on
whimsical wavelengths is the norm dancing through
the stars chasing spectrums of light It's that
time again. It's time for Whimsical Wavelengths,
where we uncover the best scientific pickup lines
like this one for all those geophysicists out
there. You must be a gravity anomaly because
you're attracting all my attention. This time
on Whimsical Wavelengths, we're going to explore
machine learning and artificial intelligence
applied to... Computer overlords rejoice. No,
really, though. Artificial intelligence and machine
learning are extremely useful in scientific applications.
No copyright issues here. What? I think, actually,
this is going to be the first time we've really
applied or really using artificial intelligence
or machine learning as part of the underlying
science. Yes, here in Whimsical Wavelengths,
it's come up a few times when talking about certain
topics on... different aspects of science, but
usually really kind of near the end of an episode
where you're like, where is this going for the
future? So where to start for today? I guess
what is geophysics and inversions seems like
a good place. Geophysics is basically combining
my two favorite subjects, geology and physics,
together, using physics to study the Earth, its
structure, and its processes. And geophysical
inversions? That one ends up being a little bit
harder to define without using a bunch of jargon.
Here is going to be our starting point. It's
going to get a lot more technical, especially
once our guest joins us. A geophysical inversion
is a mathematical process of estimating the distribution
of physical properties like conductivity, density,
or perhaps even velocity, wave speed velocity,
if we're talking about seismic data. We're looking
at these properties beneath the Earth's surface
from measurements... collected usually on or
above the Earth's surface. So obviously when
it comes to the Earth, we're not able to tunnel
really easily. So when we try to measure something,
we're measuring it at the Earth's surface. We
can't measure these properties directly in situ.
I'm not going to be there at a kilometer beneath
the Earth's surface with an ohmmeter measuring
the resistance of all these rocks everywhere
underneath the Earth. It's just not feasible
or possible. So inversions are special because
they use the physical properties or the physical
measurements that we take on the surface and
then work backwards to infer what the Earth must
be like to produce those observations through
solving the physics through mathematics. The
data itself, though, I've spent years of my life
doing this and collecting it across the most
remote places in North America. The data is typically
collected via helicopter walking across the terrain.
Me, I was doing this on the ground, meaning I
was walking many, many, many, many, many, many
kilometers or miles carrying wire and instruments
across hills, swamps and mountains and everything
else in between, pushing buttons and problem
solving. And problem solving is a really big
thing when you're hundreds of kilometers from
the nearest road or town of anything. Meaning
if something breaks, you have to kind of cobble
it back together to continue to collect data
or handling crew dynamics when somebody wants
to quit or doesn't like somebody else. My point
here is that the data can be really hard to collect.
Some examples of data that you can collect are
measuring changes to the Earth's gravitational
or magnetic field over space. So walking across
the Earth's terrain, pressing buttons, collecting
how that field changes spatially. or looking
at how magnetic fields or electric fields travel
through the Earth. Once we have the measurements,
the inversion, like I've already alluded to,
is a mathematical way of taking those measurements
and trying to get something or looking at how
different physical properties change beneath
the Earth's surface. To get a model, particularly
a 3D model of the Earth, requires a lot of assumptions
and simplifications. because there are way more
unknowns than knowns. We're collecting data at
the Earth's surface and trying to infer what's
going on deep beneath the surface. Broadly speaking,
that means we have an infinite number of solutions
or models that can reproduce the data we've collected
at the surface. Today we're going to explore
tools that we can use to limit that number or
infinite number and illuminate what we cannot
directly observe. We're going to... illuminate
the void of nothingness you know some people
fear the void or the unknown but geophysicists
just call it a zone of low sensitivity time to
dive in and bring in today's guest like myself
he spent time in rural areas of the world pushing
buttons and pulling wire across the landscape
differently though he's much more mathematically
inclined to build the tools i'm privileged to
leverage to probe the earth so Today, please
welcome my co -worker, PhD candidate at University
of British Columbia and geophysical guru, Jonathan
Kataj. Thanks. Thanks for having me on the show
and such a great introduction. That's great.
Short and sweet, but we'll get into all of your
backstory in just a second. First, PhD land,
at least that's how I called it when I was there,
using as little jargon as you can, which is...
Always tough. Describe what you expect your thesis
will aim to answer or investigate. Yeah, well,
I guess as simple as I can. Yeah, so I'm using
image segmentation methods, which are usually
not typically used in geophysics. And I'm trying
to use them to answer the questions of how we
can resolve our subsurface images better, given
our data. We tend to use really... kind of just
regularizers and they're pretty general that
we're just looking for smooth models and the
images are they're not so much blurry they're
a whole bunch of stuff they're a whole lot of
everything but we can uh but what i'm trying
to do is use segmentation methods um to help
resolve that okay image segmentation methods
what what what are those i mean i'm playing koi
because i i I know more than your average person,
but I don't feel like the full explanation, like
what is, everyone knows what an image is. So
what is image segmentation methods? Like give
an example from, I don't know, using some AI
thing, like recognizing streets or something.
Yeah, well, we could even relate it to self -driving
cars. They've got to take their images or take
in images and be able to isolate structures.
So I guess if you want to put it down to it,
image segmentation is just trying to identify
areas of commonality, and it kind of gives boundaries
to objects and structures that you are able to
then interpret. Okay, and then you're using this
to look inside the Earth. i guess you're trying
to use images of the 3d models that you can build
from doing things like geophysics that we infer
that they're fuzzy and then you're adding this
process to try to infer structure and boundaries
instead of something fuzzy Yeah, exactly. We're
just trying to find these edges or just highly
define these structures in the subsurface. And
by using these image segmentation, we can kind
of refine and improve our regularization to be
able to pull out more like geologically real
models. All right. Another deviation. Regularization.
What is it? Yeah, it's pretty much the term in,
well, I'm going to probably use some more jargon
here, in the objective function. So some kind
of function that we want to minimize. But yeah,
to make it easier is the regularization holds
all our prior assumptions, all the knowledge
that we know or what we expect for an outcome
of the model that we try to create. So you can
use this word regularization on any problem that
you're adding. prior information on, whether
it's topography or anything that you're trying
to... I keep wanting to use the word invert,
but I'm also trying not to use jargon. So any
problem you're trying to solve through mathematics,
the regularization is providing some kind of
structure to the solving of that problem. Is
that fair to say? Exactly. We're just trying
to give it as much information as we can on what
we want to expect out of the model. Awesome.
So now we're going to... back up before we go
forward, because to get to PhD land, your journey
wasn't exactly a straight line. So what was the
winding path that brought you to this point of
the world? Oof, that's a long story. Well, first,
well, kind of similar. I really liked dinosaurs.
I really liked volcanoes back when I was really
young. But, you know, then I kind of just wandered
into math and science land and thought I would
become an engineer first so I tried that and
then yeah then I found a geophysics class and
it really hooked me so I decided well I'm gonna
go and do my bachelor's in geophysics so I went
and did that and after four years of yeah first
doing engineering and then into geophysics I
really wanted to get into industry so then industry
I joined yeah an active data acquisition group
and just toured the world collecting data all
over the place. All sorts of data, mostly what
we call direct current resistivity and induced
polarization, where we're just ejecting lots
of current in the ground and mapping electric
fields. And then from there, I don't know, I
guess I got a little bored. So then I got into
programming and then I started writing software
to do signal processing. And then I joined another
group that had a lot of data. So at the time...
the stuff that we use, like the software that
we use to interpret that data was insufficient.
So that made me get into Inversion, which I joined
a group called Simpeg. It's an open source group
out of the University of British Columbia. And
I played around with them for a while, which
then, you know, they kind of grew on me. And
they're like, well, John, why don't you do a
PhD? So here I am. Yeah, I guess that's one thing
we definitely share in common. Although I got
to say, I was only really within Canada when
I was traveling about pushing buttons. You really
got to go the whole world. So at some point I
will circle back near the end and you're going
to have to give us a story of the craziest place
you've ever gone or the craziest event without
giving too much away so you don't get in trouble.
So there's always, as we've been kind of alluding,
like you went and collected the data, then you
got into inversions. So there's always two sides
to this, two parts, which might be a whole side
discussion, the data and what happens afterwards
or in our case for our day jobs, the inversion.
So let's dwell on the data just for a moment
before we get into the more technical stuff.
Like how is the data collected generally? Like
what's involved? Well, it all depends on the
survey itself, but the ones that I've been into,
electromagnetic surveys, oh man, yes, it's very
impressive the logistics that you have to do
to carry out one of these surveys. Bringing in
kilometers and kilometers of wire, dragging that
through bush, you know, there's not always a
path. So you're going to be in snowshoes, climbing
hills, swinging from vines. Maybe not swinging
from vines. Well, actually, there was a time
in Mexico, we were in a pretty steep topography
and the vines were helping me climb those mountains.
So maybe I wasn't swinging from them, but I was
holding on for dear life. Okay, so it's a lot
of hard work is really what it comes down to
in some really remote and special places sometimes.
So inversions, building off what... you've already
read in the intro, I set a basis for what geophysics
is and what an inversion is. One term that I
danced around on purpose was the term ill -posed
problem. So in our context, in geophysics, what
does that mean? Oh, boy. Well, the technical
term would be it's the null space, the space
of infinite kind of models that we can actually
fit our data. simply what I could put it as.
Sure. But I don't think anybody outside of taking
university math has ever heard of a null space.
So would it be fair to pose it as if there's
more unknowns than knowns? You have less real
world data and more things you're trying to solve
out of an equation. So there's too many possible
solutions to solve that set of equations. Exactly.
It's just an underdetermined problem usually
is what's the case where, yeah, again, you're
solving for this model that's discretized up
the subsurface and it's usually millions of parameters
and you might only have, you know, dozens of
data. So that is exactly what it is. It's just
very underdetermined. You're trying to solve
something with less knowledge than what parameters
you're trying to solve for. Right. Wow. You've
also just recently got your first paper published.
It is published now, is it not? It's on its way.
It's on its way, but it's been accepted. Is that
where it's at? Yeah, it's just that they, yeah,
long story short, they're changing the system.
I have to submit to the new system and then it
goes through. Okay, so it's accepted, but has
more bureaucratic hoops to go through. Oh, you
bet. Okay, so that paper, which is the first
of your PhD, so congratulations on getting that
through the system because that's always a pain
in the butt, is learning structural information
through inversion using image segmentation methods.
So first, where did you submit it, which is a
bit more of a question of the sausage of science,
and why specifically there? Yeah, so I submitted
to the Geophysics Journal. So that's kind of
run out of the SEG in the, well, I guess it's
very international, but it's the Society of Exploration
Geophysicists. And yeah, I submitted to that
one primarily because a lot of the work is based
off of papers that were published in that journal.
The original one was by Doug Oldenburg and Yago
Lee. And they were looking at how do we form
these regularizers to contain more of our information,
our prior knowledge. And they found a way to
be able to take its principal axes. And when
I say principal axes, it's kind of like your
model space. And we live in a 3D space, so we're
going to have our x, y, and z axes. But in mathematics,
we can change that. we can rotate that at coordinate
axes so that it's kind of pointing a little bit
down a little bit up and that's essentially the
work i built on is upon theirs because now i
have a way to be able to take you know spatial
information and prior knowledge of dipping directions
or like straight layered stratigraphy and i can
program that into every cell in the model so
that i can tell the regularization exactly I
need to be able to, I want structures in this
direction. And that's also another common, or
maybe not a common theme in geophysics, but a
lot of the geophysics papers are kind of the
application style. It's not so much the theoretical,
but how we can take that theoretical and then
add it to our toolbox. OK, so I guess that answers
the question of why you put it in geophysics,
because the people that or that journal really
specializes in these sorts of methods based papers.
Yeah, it's an application of most of them. They're
like it does do theoretical, but a lot of that
is like the papers that I followed up on all
structurally built. All the structure that I
built from is from this geophysics journal because
it's an applied journal. That's what I'm doing
is I was trying to find a way to apply some of
this APRO knowledge to an application. Okay,
so we're going to do one of my favorite things
to do as part of the show and take something
I had no idea what it was when I first read it.
Spent a lot of time trying to figure out what
it actually meant and then still not sure. But
better off than when I started, to be fair. So
we're going to do some fun sounding jargon. Applying
a Gaussian mixture Markov random field. I bet
most people here have at least started listening,
have heard of a Gaussian distribution or normal
distribution. It's probably the distribution
characterized by, you know, it's the symmetrical
bell shaped curve. Fun fact, this is how some
professors curve grades. You can totally. dump
on Gaussian there. Grade's too terrible? Yeah,
just adjust it so it fits the Gaussian distribution.
In my own experience, in actual fact, it's rarely
done. I think I've only had it done three times
out of every single course and test that I ever
took. But aside over, what is this thing? A Gaussian
mixture Markov random field. Yeah, I really just
go with the GMMRF. Even that one's hard to say.
It's just, yeah, it's a mouthful. But yeah, essentially
it is, yeah, it's a special kind of Gaussian,
I guess, Gaussian distribution. It contains multiple.
So it's going, you're going to have many means
within this Gaussian mixture model. So it's,
yeah, it's pretty much what the name says. It's
a mixture of individual Gaussians. However, what
makes it a Markov random field is that you can
encode spatial information into it. So those
Gaussians know how to talk to each other in space.
So you have a bunch of bell curves that are distributed
in space that can modify each other. Yeah, depending
on how close they are spatially, not just necessarily
how close their means are. They could have close
means, but they could be very far apart. And
these Markov random fields help us be able to
encode that into our Gaussian mixture model.
Okay, so the idea is that your mathematical algorithms
or formulas or equations, the inversion in other
words, has a series of these Gaussian distributions
within it due to structure a prior information.
Yeah, typically it'll be like a physical property.
We'll have an idea of how many kind of rock units
that we're going to expect, and those will be
assigned a physical property value, which stands
for a mean in our Gaussian mixture model. Okay,
so you can imagine... Audience, correct me if
I'm wrong, if I overstep my skis here. I've been
known to do that. But you can imagine if we just
take a layered earth, which is rarely the case,
but if each individual layer had a different
mean, each one of those represent a different
Gaussian, and each one of those is also representing
a structure. And so you have these individual
Gaussians or layers. that can affect one another
within the inversion or the ill -posed problem
we're starting with. Is that kind of right? That's
pretty bang on. Yeah. Perfect. Okay. So when
we start talking about structural information,
which is almost a jargon term in itself, because
to those that are just jumping in somehow into
the halfway through the podcast, you might be
thinking like we're building something. Is it?
It's definitely not like creating a city under
the ground, though, is it? No, I wouldn't say
creating anything underground. It's just trying
to interpret from the data what this subsurface
image would be. Okay. Maybe to bring this together
and make it make sense of why this is kind of
a big thing, like it is actually a huge step
forward for... people in our sphere for inversions
is let's go back to what a classical geophysical
inversion is also known as the unconstrained
inversion when it comes to imaging geology i
know we can image or illuminate it really well
but generally where do they fail uh well you
know they fail a lot mostly because of their
sensitivity, depending on the data set that you're
using and what you're trying to detect. So we're
going to have lots, like, again, this comes into
that ill -posed and this null space. There's
so many models. So we've got models that can
have very thick, say, conductive, so meaning,
you know, currents travel really easy in these
layers. And if that's over top of anything that
we're actually trying to target, we're not going
to be able to see it. It's going to suck up all
that kind of imaging power, all that current,
and we're only going to see some kind of conductive
layer. So these really break down because in
a standard L2, you've got no other way. You're
just like, I want a smooth model. So you're just
going to get this smooth conductor on top. Whereas
when we can start modifying... that L2 and putting
some more information into that regularization,
we can start kind of see like, okay, we expect
something to be shallower, but we want stuff
to grow at depth. That's where we're going to
get our advantage over our standard L2, as I
call it, or the unconstrained inversion. Yeah,
maybe this is like, again, I'm very much a user
audience when it comes to inversions. I am much
more of a closer to the geology side of geophysics
than what John is. much within the mathematics
and able to program some of this so um i'm gonna
take a stab at this a little bit to illuminate
the fact that we've used the term l2 and uh that's
just if my understanding is correct and hopefully
it is it's just another way of putting in uh
an um Our prior information, we're making an
assumption to be able to solve the ill -posed
problem. And L2 is a way of bringing in that
assumption, which assumes kind of a continuous,
smooth property distribution. So it's going to
try to smooth it out a little bit. It's not going
to give you true layers. It's not going to give
you hard boundaries until you do something to
it, in which case it's no longer constrained.
Is that about right? More or less? Yeah, that's
completely correct. Because as soon as you start
wanting more out of the inversion for details
and boundaries, you're starting to put in, you
want to put weights or hard constraints at certain
places and certain spatial areas in the model.
So in other words, the L2 is... It is fantastic
when we have no other information. It provides
us something that can track geology fairly well,
but it's always a really kind of fuzzy image
of what the real thing is. It's like, for lack
of a better way of saying, it's like looking
through a number of different glasses and each
one is distorting the image a little bit more
and a little bit more. So it kind of blurs it
out. But in geology... it's often quite a stark
contrast when you hit a lithology boundary you're
really going from one property to the other so
this smoothness doesn't really break down so
perhaps it would be useful to state why we don't
typically force that onto your standard unconstrained
inversion Yeah. Well, the way to explain it mathematically
is that the L2 is just a beautiful function.
It's differentiable everywhere. It's smooth.
It just works mathematically nice. When we want
to start imposing in some of these prior information,
or say we want to use norms that aren't smooth,
that gets mathematically hard. And it doesn't
always want to converge. You don't always get...
to an actual model that satisfies the data. Well,
let's take a tact back towards your paper. There's
lots more we can get into, but I think for understanding
the paper, we need one more component before
trying the results. Neural networks, the AI or
machine learning component of the paper, which
if you're going down that road, I really hope
you're not working at replacing me. No, no, no.
No, definitely not. Most of the work that I do
is we're pulling it to be a tool of ours. So
that's the thing. What my paper was built off
of is rotating that regularization, being able
to encode the information into it. So technically,
you don't need the segmentation or the AI. If
you have an idea, you can put it in yourself.
But what I'm working at here is being able to...
Let's make the computer do the work for us. Why
do we want to go out and measure dips and look
at orientations and spend the time assigning
these ourselves when we could actually have just
a model that can take that information or take
in the information that we have or the images
that we have, geology, and then it can spit out
information about the orientations and the structure
directions. Okay, so I guess there's two questions
overlapping here. First, so you're getting the
AI or machine learning to pick out an appropriate
structure to try to seed or use as prior information
to the inversion. But like any machine learning
or AI, you've had to train it on something. And
I assume you haven't stolen a bunch of inversion
models from across the internet and trained it
on it. Maybe just explain that part a little
bit for the audience. Yeah, well, at first, that's
the avenue I started to look at is, well, you
got these models, you have to train it on something
that you're looking at. It's not very generalizable
when I first started. But then trying to make
examples, it takes a long time to run inversions.
So getting a huge sample base was not really
a thing. It was just not tractable. So, no, I
didn't steal anything. But what I did was, is
I've Actually, it was the spring, right before
I started getting into this, the SAM model, Segment
Anything model from Google, or from Meta, was
released. And it looked really great. They were
trying it on all sorts of images, all different
sorts of things. I'm like, well, let's take a
crack at geology. Let's see if it can look at
a geophysical inversion and segment objects.
And lo and behold. It actually worked right,
really well, right out of the box. And with these
models now, they train them on the whole internet
or whatever have you, what have you. And yeah,
they put these models, these checkpoints up and
you can download them and you can start from
there. So even if I wanted to, or I could, if
I wanted to, is then use some of the... geophysical
inversion kind of examples i had before and i
can fine tune it but i found that i actually
don't have to the sam model is quite great and
it it's a great segmentation tool whoa wait a
minute so you just took something that is trained
for something else completely something i mean
it's similar you're finding structure and identity
and shapes and things yeah but you didn't you
could just take that and apply it directly into
this branch of science and you didn't really
need to tinker with it? Yeah. So they call it
foundational models. So I've found a foundational
model that's applicable to the geophysical inversion
and tried it out and it works. Something that
was trained on ice cream trucks can actually
also find segmentation in geophysical models.
Wow, that's awesome. Okay, so now the results.
Because we've got the ill -posed problem, how
we're going to add information to that ill -posed
problem through image segmentation. So identifying
things that are created in the model by solving
that ill -posed problem, things that have structure,
and applying that structure and essentially rerunning
the inversion, I guess. Yeah, okay, I saw the
head nod. So you're going to go through that
process. So what is the case study or proof of
concept for this? Set the scene for us. Where
was the data collected? So the data is collected
in the wonderful Athabasca Basin. Oh, I know
it well. We both do. Nice cold winters and lots
of skidooing and fun. And a lot of bugs in the
summer. Holy smokes. For those listening that
don't know where we're talking about, the Athaka
Basin sits in northern Alberta, and it's actually
mostly in Saskatchewan, but it straddles the
border there. It's got a big lake. It's a sedimentary
basin, thick sandstone layer above it. Sandstone
high in quartz. Quartz is a resistor. And then
beneath it is the old, old rocks of the Craton.
So the oldest rocks of the world actually sit
in the North American Craton. I don't know about
the area of Craton here, but it's certainly more
than 2 billion years old. I just can't remember
how old the Craton is beneath the Athabasca Basin.
But back to setting the scene. So in this setting,
we're going to have lots of structural faulting
happening across that interface between the sandstone
and the basement. And this allows hydrothermal
fluids to drain upwards. And they carry a lot
of hydrothermal fluids that are going to mineralize
and deposit uranium. these boundaries or near
this unconformity. And these conductors are really,
these faults are really huge. So they form these
long dipping conductors or yeah, they can, they,
they form along the faults. So they're, they're
not quite sheet like, but they're, they're very
dipping like objects. And that is essentially
what a nice, a great kind of object for image
segmentation. And the idea is, is we want to
be able to tell. where these conductors are faulting
to or where they're dipping to so that we can
plan the drill holes accordingly. We kind of
know which direction to step out as we kind of
probe these conductors. And yeah, then we also
have these horizontal kind of... unconformity
over top of them so if we can start looking at
we can start segmenting structures that are not
flat lying and we can get kind of a dip approximation
out of those and then we can also get kind of
the the thickness of that resistor on top as
kind of a segment outing like flat layered blocks
all right before i i try to summarize the whole
picture here because i think you've done a good
job for me um how thick is the uh the overburden
here It varies a lot, but generally, yeah, generally
about 500 meters in the areas that I'm looking
up for. So yeah, it's quite deep. So for all
the listeners, you can imagine you have, because
it's Saskatchewan, it's pretty flat. So you have
flat plains, lots of lakes and trees and swamps.
And then 500 meters, the top 500 meters is basically
all pure quartz sand. Beneath that 500 meters
is a surface that we call an unconformity in
geology. That's basically where we've had erosion
cut and just remove material. So you have a gap
in age between the sediments above and below,
or in this case, above and the rocks below of
the craton, the metamorphic rocks. In that metamorphic
rocks, there's a series of faults. And those
faults have carried hydrothermal fluids, which
have deposited nice... conductive minerals and
which is what we're targeting here right exactly
it's these conduct we're not targeting the uranium
or the uranite exactly but it's the associated
mineralization components which is these big
graphitic conductors yeah and graphitic just
means as it sounds like it has graphite in it
it's got lots of carbon in it um and these because
they are bound by these faults and the faults
are roughly linear they don't do any weird geometry
they provide good structural basis they should
be somewhat simple for the image segmentation
is that correct Yeah, and again, maybe depending
on the inversion, or sorry, the cervic type that
you've used. So far, I've experimented with magnetotolerics
and DC, or I guess DC resistivity. The DC resistivity,
you know, if you can't quite image all the way
down to there, down to the basement, it's going
to be hard to get something that's going to segment
out properly. However, in my... case uh it has
been working out not too bad and that's uh but
that since given that limitation because with
the sensitivity just decreases with depth for
that kind of survey the magnetotelerics though
gives me that definition i can get that resolution
at depth so that's so far for the basin kind
of dipping conductors it works really well unfortunately
that's not the most economical survey or easily
or easy survey to do in the basin All right.
And you use two different surveys. They're direct
current, where you stick a bunch of electrodes
and pump a bunch of current into the ground.
You measure the voltage between points. And then
magnetotellurics, which are... It's a passive
method. So we're using sources that are, you
know, lightning storm down in the Amazon or solar
kind of... solar influence it's kind of the reverberation
of the magnetosphere and the signals kind of
trap in with from there and then those travel
in plane waves and those plane waves hit our
ground and we can measure the different frequencies
and each frequency corresponds to a different
depth given a conductivity so i assume then because
of the differences in frequencies you can look
at really long wavelengths or to look deeper
than what the direct current can, and that's
why you can see these areas better? Exactly.
So if you record long enough for these MT and
these low enough frequencies, you can see down
to kilometers, even tens of kilometers. Not here,
but the general surveys around here are low enough
frequency that we get a good couple kilometers
easily out of them. Okay, so you were able to
segment and get these structures. And how did
it line up with what has been actually ground
truth, which basically means drilled 500 meters
down to intersect these things? How did it compare?
Yeah, I'm pretty fortunate. The survey that I'm
going over and using the data from has been extensively
drilled. It's a mine already. So I had a lot
of good ground truth. And lo and behold, it worked
out very well. Compared to the standard L2, as
we were talking about before, that smooth assumption,
you really didn't... Like, you couldn't really
tell. You can see that there's a conductor. There's
maybe a break in the unconformity. But you'd
have no idea if it was dipping to the east, dipping
to the west. Where do you plan your drill? Do
you just drill straight down on top of what you
have? You're not getting a lot of information.
Again, this is 500 meters here, so this is expensive
drilling. It's not just cheap just to send a
drill down. So we really need this information.
And also, we've had seismic in this area, which...
lights up really well as kind of like another
ground truth. And with the image segmentation,
being able to just kind of, it can see a lot
of things. Like it looks at data, not just like
a visual eye. So it can kind of find those boundaries
a lot better, you know, given maybe tweaking
some parameters. But with that little bit of
influence, the regularization can be like, okay,
I have something here and it actually drives
it home. It doesn't, it can kind of, for lack
of a better term, latches on to the solution
and keeps it pushing from there. And that was
able to recover. Maybe the dip was about pretty
shallow, about 36 degrees. My estimation was
about 40, 39. I'd say it was like 39 .8, but
I'll maybe have to call it 40. But yeah, in hindsight,
yeah, significant figures, exactly. But in hindsight,
it worked really well in this application. Yeah,
I mean, to be fair, that's close enough for what
they needed to do. They need to know rough direction
and, you know, plus or minus 15 degrees. If you
get within that, to be honest, at 500 meters
depth, you're doing amazing. It's a bit like
trying to throw a dart blindfolded, right? 500
meters away, and now you're giving them, you're
taking off the blindfold. It's still 500 meters
away. You still got to be able to hit the spot,
but you're increasing your chances for sure.
That's pretty good. I mean, I think this is new.
And in our opinions, we've opined on this over
beers. The industry working is so slow in adopting
new technologies and techniques. Will we see
this any time soon? Ah, well, I'd like to say
yes, but I have moved on in making the... Well,
I've kind of rounded out some of the edges that
kind of... came out of this this study as it
is but yeah it's it's kind of funny because the
industry actually i don't think realizes how
close they are to being able to do something
like this the this code like was back the paper
that i got the original idea from was from 99
and it's actually buried in some of the software
that some of these industry leaders use but nobody
really understands or knows how to use them.
Or maybe it's a lack of willingness to do it.
These tools are all here, but they're more, I
guess, like Lego blocks. You've got to put them
together yourself and design it. But all these
tools are available. But having someone push
it and put it to a point where it's like, oh,
I want to do image segmentation with structural
information incorporated button. That takes a
little bit more than just the science to make
that stuff happen. So, in other words, we have
the tools, but we don't have a user interface
to play. That's essentially it, unless you're
willing to kind of dig into the blocks yourself
and start building it. That's the unfortunate
bridge where I guess there's the academia and
then there's industry. So you're going to build
it for me? Well, actually, I think we do have
it in our codes. Well, at a level that I can
use. I mean, that's a pretty high bar, I know,
but because around work for the listeners, I
tend to break all the codes. It's just a skill.
I'm a bug tester by accident. Yeah, it's a good
thing. It keeps our codes pretty top -notch.
OK, so what about AI and machine learning more
broadly? I mean, we're talking about toys. We're
talking about different how our industry is moving
slow forward in a slow space. But it seems like
AI, the possibilities are broad for use. And
I just can't imagine somebody not using it. So
is it going to be used more broadly or is it
already being used more broadly? Where is it
going? I think it is and it isn't. Like you said,
there's... The industry seems like it's slow
to adopt, but there's also people within that
industry that want this stuff that, you know,
they're paying money for the academia to research
it and to make it happen. And yeah, I guess that's
kind of where things, well, hopefully they're
going to go is that we're like, from my experience
on being part of this impact group and being
at the university of British Columbia, we're
trying to take. take all that stuff and make
it more simpler so like even my next work after
this paper is incorporating it more into being
like more like a geophysical inversion where
you can be like okay here's my data here's the
regularizations I want to use here or here's
my assumptions and then invert and then get our
outputs. So we're, I guess they, it's pushed
academia enough that we're starting to like,
look at the problems that way so that how do
we, how do we transfer that knowledge a lot better?
So I think from the academia side, we're definitely
pushing that way and industry, it's kind of up
to them to hopefully keep up with what we're
doing and pull it in. But you've, yeah, the industry
is so, so variable. Some people like what they
have and just going to stick with it or yeah
there's the people who are out there who want
to adopt it and are kind of keeping up with what
we're doing a lot of say these newer companies
that are coming out that are more like tech mining
companies they are full in both that dove in
with both feet and they're even on to the next
level of doing things like prospectivity mapping
And that's incorporating more data than just
geophysical data. So seeing the adoption of that,
but until we start seeing, I think, a lot of
outcome from that, because I don't know, I can't
really, some people are saying they found deposits
and they're finding things with AI, but I don't
think that there has been a mine that's going
to start anytime soon that was found by pure
AI. But until I think on that step, until they
can start proving results. Yeah, well, it's because
they're also looking for something. They use
a mine as a reason to, oh, I found the mine.
Well, yeah, you found the mine. We knew it existed
30 years ago. Like it's already mined out. You're
just finding a signature. It's like I've seen
geophysicists do the same thing. And they're
like, oh, we need to invert and drill that. And
you're like, no, that's the mine. There's already
buildings there. It's this little spot over here
that we're interested in So yeah, I wonder where
that's gonna go and whether or not our techniques
are sophisticated for it I also should really
at one of the times go into like a historical
look at my own industry and inversions because
what we used to do back in like the 60s is wild
all the inversions were done in people's heads
and it really came down to who like who you got
for a particular area because the inversion was
all in their brain matter it was not a repeatable
solving of physics unlike it is today today it's
a real science back then it was a dark art holy
smokes but um before we move on and i get into
the last couple of fun questions is there anything
you want to call out from uh Anything from your
project, your ongoing PhD work, or maybe the
Simpeg group itself? I don't know. Anything you
want to call out and bring up at this point?
Yeah, just I guess to promote a little bit of
my future work. So the first kind of work here
is building on, you know, what is image segmentation
and how can it be applicable to geophysics? And
so I find a way to be able to apply that. and
you know we can get some results we can use that
information but it's kind of something that's
abstract outside of the inversion it's not really
incorporated into it where my new work is now
using actual like this kind of the same equations
that they do to do image segmentation problems
well with images say like deblurring so if you
take a picture of a fast moving car you get a
picture of the car but everything behind it is
a little bit blurred so there's techniques that
you can use that use image segmentation to correct
that and then you can actually kind of get your
background resolved better and upon reading those
papers they've been applying it to medical and
all sorts of other applications. And now that
I've now taken that and adopted that to the geophysical
inversion. And then now it's kind of getting
to that where you were saying, like getting it
into the hands of someone, maybe an industry,
they can use it a little bit easier. Maybe they
don't need to understand all the crazy math behind
it, but it still functions exactly like the tools
that they're kind of used to. And yeah, and that's
all done through the Sympeg framework where I'm
actually part of the new initiative where we're
kind of. looking back at the optimization tools
within there. And we're redesigning that so that
we can plug in my ideas or even any others, like
even using neural networks as regularizations
themselves being put into the SIMPEG framework
there. Cool. Okay, so I want to ask... One more
joke before, or one more joke. I want to ask
one more question. Before we get to the joke,
you can see how excited I am for his science
joke. I am too. Okay, so first things first.
Just, I know this is going to put you on the
spot. And I know sometimes, because I've been
in the field myself, you don't necessarily want
to give out too much information to get somebody
in trouble. But collecting this geophysical data
is... special in its own right like where you
are the people you're dealing with the locations
the logistics it can all be incredibly special
sometimes it's not but quite often it is so perhaps
a short story or an example from your own time
in the field Oh, boy. Well, let's do a fun one,
but also a devastating one. Oh, it's both a tragedy
and a comedy at the same time. Shakespeare would
be proud. So I did actually a lot of work in
China. We were one of the geophysical contractors
that actually would do work and people were able
to get us in. We had an entity there as well.
And yeah, it was in northern China. Kind of an
old gold mine. So, you know, it's kind of more
like maybe the foothills of Alberta. But again,
everything has thorns on it. So everything wants
to scrape you up and do all sorts of things.
But, you know, it was really fun because there
was it was a. It was a really large scale survey.
We covered a lot of little small towns. And,
you know, the helpers that came and helped us,
they would invite us over to their house and
have meals. And, you know, it'd be like they'd
be giving us apples, all these fresh fruits in
their fields that we're going through with our
wires. And yeah, it was just generally a really
good experience, except for when the mine site
owner would come. And this guy, someone would
hear his name and they're like, oh boy, we have
to run. But it wasn't because he was a scary
guy. It's because this guy really liked to party.
And he didn't care if you had to work the next
day. He didn't care if you had a deadline. He
would take pretty much everybody from the mine
out and you'd go have hot pot, karaoke. And a
lot of Baishal. So much Baishal. And I'm not
going to name names because that's just to keep
things a little general. But yeah, it was always
scary because when you heard his name, you didn't
want to go out that night because you're like,
I have to go hike 30 ,000 steps minimum tomorrow.
And I don't want to. And, you know, when you're
in different countries, you know, you... Sometimes
saying no is not culturally accepted. So you
kind of have to say yes. So it was funny because
people would run and hide and they would try
to avoid these situations. It was like, oh, sounds
fun. It does. But after, you know, once every
couple of weeks, this guy comes around, you're
just like, oh, I got to hide under my bed. Yeah,
for sure. Like, it's not it's no joke. You say,
yeah, you know, you're young, you're fit. You
could do that one time. Yes, but you have to
be doing that 30 ,000 steps or depending on the
terrain, it could be, you know, 20 kilometers
of walking or it could be like 10 kilometers
of walking, pulling wire, bushwhacking, whatever
it is, but you're doing it day in, day out. Day
in, day out, for weeks at a time. Sure, any one
of those days. If you're relatively fit, not
a big deal, but try doing that for six weeks
straight. It's special. Oh, yes. So it sounds
like that was an adventure, both fun and it wasn't
very tragic. Slightly terrifying. Unless you
skipped over something tragic, in which case
we'll leave it there and we'll go straight to
the final question because we're going to keep
on the comedy instead of the tragedy. And your
favorite science joke. It might not be my favorite,
but it's a segment, my favorite segmentation
joke. Oh, even better. The nerdier, the better.
Come on, John, talk nerdy to me. Okay. All right.
So why don't segmentation models ever fight?
I don't know. They always find clear boundary
first. I tried to make it as terrible as possible.
I don't know. I don't know. I love it that it's
thematic. It's even better. All right. Well,
thanks so much for being on, John. It means the
world to me. And for all those listening, he
is one of the few people that have been listening
to this podcast from the beginning. I'm so glad
I finally got him on the podcast because he's
awesome. Thank you very much. Not too bad yourself.
Thanks so much for being here and enjoying Whimsical
Wavelengths with me. Of course, this is the time
of the show where I ask you to subscribe, tell
a friend, reach out to me on social media. You
know, I actually had somebody suggest on Blue
Sky that I should do a show all about why mammals
weren't, wasn't able to take over the skies.
We managed to take over and become apex predators
in the marine sphere, as well as everywhere on
land. Why not the skies? It's a pretty good,
interesting idea, getting into the evolution
of flight and just how evolutionary pressures
really didn't favor mammals in that way. I don't
know if I'll get around to doing that, but that's
a really cool idea. I love it. Keep it coming.
I'm serious. So yeah, subscribe, tell a friend,
and I'll see you in two weeks. So chaotic, so
misbehaved. Echoes of melodies remind us it's
always. Colors weave stories painting the sky.
Swaying to rhythms as the galaxies fly by. Chasing
down wonders, always seeking. Huh?