- Earthquake prediction is
one of the grand challenges in Earth Science.
As a colleague of mine once said,
"If there's ever going to be a Nobel Prize given in geology,
"it will be to the person who figures out
"how to predict earthquakes."
(fragmented news reports)
(building ominous music)
- Earthquakes, of course, cause very strong ground shaking,
that can cause landslides, liquefaction,
building damage, and even cause deaths.
- This is a very emotionally-charged topic.
People are jaded, they're tired of it,
and they're suspicious about any publication
that might claim they can predict earthquakes.
- We've never been able to predict anything
by exactly when the earthquakes are gonna occur,
or how big they're gonna be.
- But now, with machine learning,
all I can say is we're gonna make dramatic advances
that are gonna surprise all of us.
(ominous music slowly fades)
Five, six years ago, I was just really puzzled.
I was scratching my head about where are we gonna go?
There was no indication that we would see
any kind of breakthrough regarding earthquake forecasting,
or really understanding how a fault worked
between earthquakes.
- When people talk about earthquake prediction,
what they mean is predicting the time,
location, and magnitude of a big earthquake,
hopefully with enough skill and precision
to be able to actually do something about it in advance.
(radio static)
- There's lots of things that seem like
they might be useful ways to predict earthquakes.
Planetary alignments, lunar phases, earth tides,
animal behavior, but there's not turned out to be
anything in those observations that tells us
about future earthquakes.
- Geophysical studies in Earth are challenging
because the Earth is opaque, right?
You can't see into it.
And one of my colleagues said, "Well, why don't you apply
"artificial intelligence approaches to the seismic data
"in particular that you're looking at,"
and that was the beginning.
This is acoustic data recorded on the earthquake machine
at Chris Marone's lab at Penn State University.
Chris Marone and I have known each other
for quite a long time now.
A deep thinker and one of
the world's leading experts on earthquakes,
so it took time to convince Chris
what we're saying was really real,
and as he understood more and more about it,
it became apparent to him that, yes,
this was the next major step we all had to take
to include these tools in what we're doing.
(machine whirring loudly)
- Part of the advantage of working in this lab
is they have an espresso coffeepot.
Guys you ready?
- Yeah, all good.
- How we doing?
- Good.
- Is that off 21?
(rapid piano music)
- So this is the earthquake machine.
It is a servohydraulic biaxial deformation machine
that's used for lots of kinds of experiments
related to earthquake physics.
There's two hydraulic cylinders.
There's this one and there's another
hydraulic cylinder that's right here.
In the experimental geometry what happens is
I'm gonna push on both sides of this fault zone,
and then I'm gonna drive the center of it down,
under controlled conditions.
You can hear the motion and what you hear
are the micro-earthquakes that are occurring
during the frictional sliding that occurs in the earthquake.
- I think most people would ask the question of like,
"Huh, if we're gonna study friction at the scale of
"the palm of my hands, how does that have anything to do
"with what happens on a real fault zone?"
But the parallels between what we see in the lab,
and what occurs on tectonic fault zones,
are just too close to ignore.
To study earthquakes, one of the things that people do,
and that's been done for a long time is
to capture the seismic energy that's radiated
from tectonic fault motion on Earth.
There's so many little acoustic emissions
that are occurring simultaneously,
that when you look at the record,
you don't see an individual earthquake.
You can't really make any sense out of it,
and so people just ignored it for a long time.
Machine learning changed all that.
Machine learning sees inside the noise.
- The first breakthrough finding was
a signal that we had discarded
as noise from the laboratory experiment,
and had just thrown away, was rich with information
about how the fault worked at all times.
So we understand we can use 100% of the data
instead of 5% of the data,
that's the huge advance we have here
because of this new toolbox.
Secondly, using the same information,
you could really nail down when the next earthquake
would take place in the earthquake machine.
- When they got to the point where they started
to say to us, "Hey, we can predict when
"the events are going to occur,"
I was like, "What? Really?"
- In practice, the way the algorithm is built
is we measure the fault friction at all times.
So we know the answer, so that's how we build the algorithm.
So with the algorithm, we try to predict the answer,
we compare it to the known truth,
and once it's working really well,
we say the model's working,
then we go to data it's never seen,
and very soon after that,
they were able to predict when the next earthquake
would take place in the earthquake machine.
That was astonishing.
So the question is would this scale, at all, to Earth?
We had no idea.
You never have an earthquake,
or you rarely have an earthquake that takes place
on a human time scale.
Most are 50 to 100 to 1000 years apart.
So what to do?
It turns out that there's a certain kind
of slip on fault in the earth called slow slip.
Slow slip is a slow earthquake.
It takes place, just as it sounds, at a slow time scale.
Instead of abruptly over seconds to minutes,
it takes place over days to weeks to months.
We had slow slip events beneath Vancouver Island,
and that's why we went there
for the next phase of the study.
Bertrand and Claudia were leading the study,
and they took the seismic data from Vancouver Island.
Using those data sets, the machine learning model
was built in the same manner it was in the laboratory.
So they trained on 10 to 15 of these slow slip events,
then applied it to the slow slip events
that the model had never seen before.
Incredibly, remarkably, we saw the same result.
At any given time, the seismic signal had information
telling you exactly what the displacement was
at the Earth's surface.
Moreover, you could predict
when the next slip time took place.
So what we learned in the laboratory,
we we're able to apply directly to the real Earth,
and again, we were absolutely astonished.
We're not forecasting earthquakes now,
except in the laboratory.
What we're trying to do is advance earthquake science.
That's really what we want to do.
If we advance earthquake science,
we'll advance the science of earthquake forecasting.
- If anybody tells you that it will be impossible
to ever predict earthquakes, well that's a guess.
If they tell you that they think
that earthquakes are currently predictable in a useful way,
well they're wrong.
But we may get there.
We need to do it through the scientific process,
carefully, rigorously, it's gonna take time,
it's gonna take work,
but the rewards are fantastic,
if it can ever happen.
- I think it's important for people to realize
that we've basically just scratched the surface
in terms of machine learning techniques.
We've used the simplest machine learning techniques
to predict lab earthquakes.
There's probably a lot that can be learned
by taking more sophisticated machine learning techniques.
I don't think we'll be able to predict an earthquake
in the next 5-10 years, but I do think we'll be able
to ask, "Was there a precursor here that we could've seen?"
- This is a new set of tools that we have to embrace.
The future really is using this to push forward
new advances rapidly.
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- Hope you enjoyed this episode on earthquakes.
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