VIKRAMAN KARUNANIDHI: Hello, everyone.
I'm Vikraman.
Almost all our lives in this room
depends on making predictions, whether we make predictions
on spam detection, stock prediction, toxic language,
as my previous big speaker spoke about, and so on.
What if I tell you there's one critical area where
our prediction could save millions of lives?
And that, my friends, is predicting earthquakes.
Are earthquakes even possible to predict?
Predicting the Unpredictable-- this
is actually a title of a book written by a top seismologist.
As the book title suggests, predicting earthquakes
is very difficult and almost impossible.
One of the biggest reasons is it's not
the question of whether an earthquake will occur or not.
This is a similar scenario that we most frequently encounter
in machine learning these days.
Rather, it's the question of when it will occur.
As soon as I say that time into this,
so the first thing that triggers our mind
is [INAUDIBLE] models and other time sequence models.
But it is not that easy.
Over the past four decades, there
have been several failed predictions.
But since 2012, we made tremendous progress
in the area of deep learning.
Now, how can we leverage the power
of deep learning to understand and predict
this complex phenomenon?
Thankfully, nature communicates with us in several ways
before a big event occurs.
For instance, in case of a tsunami,
the water recedes from the shore before the tsunami occurs.
Animals can sense the ground motions filling that ground,
and they start panicking and behaving erratically, hinting
that something, a big earthquake, is about to happen.
Likewise, there's one underappreciated earthquake
precursor called the foreshocks.
Foreshocks are an [INAUDIBLE] or a sequence
of events that occur before the main event,
also known as the main shock.
They have a time frame between somewhere around 2 hours
to 30 days before the main shock.
Unfortunately, scientists have deemed
that these foreshocks are indistinguishable
from the main shock and the aftershock
and that they are currently identified
only after the entire sequence is completed.
I mean, but what's the use?
Nature is trying to communicate with us, hinting
that some big event is about to occur,
and we don't have the right tools to capture
that useful information.
And this is where deep learning comes into play.
As you can see, the highlighter takes that indistinguishable
data--
we have indistinguishable data, the foreshock, main shock,
and the aftershock.
And deep learning can help us project them
to higher dimensional space, where
they can be actually separable.
This is the framework which I used to make this prediction.
So I have a time series data, which
is nothing but that seismic wave form.
And in order to extract useful information,
I converted this time series wave form data
into a spectrograph, and I created a deep neural network,
which takes this as an input data.
While the deep learning model does this, outputs [INAUDIBLE]
this particular spectrograph before
belongs to a foreshock, main shock, or an aftershock.
And due to a limited time, I'm not
going into the details of the model.
But I'll give you a quick preview of my test results.
And so about 19 out of 24 shocks were predicted correctly.
And even though the number looks small,
actually I did [INAUDIBLE] training,
so that actual test site is super large.
And these 20 are the individual number
of the foreshock, main shock, and aftershock events.
And I would like to emphasize why identifying foreshocks
could be really useful.
Back in 2011, there was a powerful earthquake
of magnitude 9.0 that triggered a tsunami in the Tokyo
prefecture.
This tsunami attacked the power supply of the Fukushima
nuclear power plant, and as a result,
the coolants weren't able to perform.
And eventually, there was a radioactive leakage.
However, two days prior to this mammoth earthquake,
there was a powerful foreshock of magnitude 7.3.
Back then, had we identified that this event was
a foreshock, we could have alerted the authorities
that a bigger event is likely to occur in a very short time
frame and that we could have prevented
that radioactive leakage and saved a lot of lives.
Before I conclude, almost everyone
should be aware that there is a big earthquake pending
in the California region.
The last--
[LAUGHTER]
I hope this is not a breaking news.
If you read of the Bay Area, you can see
a lot of stuff going on there.
And but the thing is the last major earthquake was in 1906.
And then there's a lot of stress accumulation.
And almost all the top seismologists,
all the scientists, agree that the magnitude
will be somewhere between 8 and 9, which is super huge--
yeah, not kidding, you can check it out.
But the thing is it's not my wish either.
It's just a stress accumulation.
So with that much amount of stress being accumulated,
and with this much big progress in deep learning, so we
can actually, by using the methods that I proposed today,
we can actually predict these big events
well in advance, in a shorter frame, and save a lot of lives.
In addition, there have been publications
in Nature and others publications,
confirming that almost all the big earthquakes are
preceded by a foreshock, which is actually the good news.
So if we identify the foreshocks in my method, in real time,
we can foresee the big earthquakes.
And thank you.
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