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.
[APPLAUSE]
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