Hi, everyone. I’m Clara Yoon from the USGS Pasadena office.
And I’ll be telling you about the potential for machine learning to
really improve and modernize the way earthquake monitoring is done.
And I would like to thank my colleagues
from the Caltech Seismo Lab.
At a very high level, this is how earthquake monitoring is done
in a regional seismic network. We start with continuous seismic
data recorded at a few hundred stations all across the region.
And that data is fed in real time into software that’s running on
a bunch of computer servers. And the output of the software
is an earthquake catalog, which tells you the location,
time, and magnitude of all the earthquakes in this region.
Now, how does this software actually work?
Well, the software that we use for earthquake monitoring
is called AQMS/Earthworm. It provides real-time automatic
earthquake information. And it’s over 20 years old.
It’s been thoroughly tested and well- tuned to run during this entire time.
And how it works is, it starts with the continuous seismic data as input.
It detects earthquake signals and then picks P and S phase arrivals
and determines first-motion polarity. And so these steps are done
at every station. And the next step is to take many phases from
different stations and associate them into an event.
Then we locate that event, compute its magnitude,
determine its focal mechanism, moment tensor for larger earthquakes –
and so this – all this information goes into the earthquake catalog.
Now, the problem with this [inaudible] software is that these automatic
algorithms, especially for detection and phase picking, they’re not perfect.
Sometimes they can make mistakes.
And so our standard practice in the seismic networks is to visually review
all these earthquake solutions by human analysts.
And phases are manually re-picked if necessary.
And so here I’m showing you an example.
The green bars here on the left show you where the automatic algorithm
thinks the P and S phases should be. The P phases look pretty good, but the
S phases should definitely be earlier. And so a human analyst really
needs to go through and manually pick all these events
so that they line up and now they’re correct.
And so, looking towards the future, we really want to be able to automate this
entire earthquake monitoring process without too much human intervention.
And so that’s where artificial intelligence, and more specifically,
machine learning, could really play a big role in entirely automating this
earthquake monitoring process. So the definition of machine learning
is really algorithms that are able to kind of learn on their own by example
without explicitly being told how to do so.
And today I’ll talk about a subset of machine learning called deep learning,
which is where you have these artificial neural networks that take massive data
sets as input – so these are called training data sets, and they already
have known labels. So you already know ahead of time what the answer is.
And these will develop a model that can learn from vast amounts of data
in order to predict information about new data that it’s never seen
before in the future. And deep learning has had tremendous impacts on many
different fields, such image recognition, medical diagnosis,
speech recognition, self-driving cars, and also in earthquake seismology
and monitoring, as I will discuss today.
And so the majority of deep learning research that could be applied to
earthquake monitoring since 2018 has been done for earthquake
detection and phase picking. And that’s because this is
a really tedious task that requires a lot of human review.
It’s really ripe for automation. And what’s more, we already have
these massive labeled data sets available for training that are really a
requirement for these types of models. Because we’ve had human analysts
over the decades build up millions of examples of P picks and S picks.
And now GPUs are widely available. They get – they give us a lot of
computational power. And then we also have open source
software libraries for deep learning, such as TensorFlow and PyTorch
that are well-documented and accessible to non-experts.
So all these things together have really paved the way
for deep learning in earthquake monitoring.
So here I’m showing you one of the first examples of a deep learning model that
has been developed for earthquake detection and phase picking.
This is called generalized phase detection from Ross et al. 2018.
The training data set that was input into developing this model was really big.
It’s from southern California. It consisted of 1.5 million examples
each of P waves, S waves, and noise seismograms.
So the input to this model is a three-component waveform
that’s a few seconds long. And the deep learning model
is able to automatically extract the most
important features that really distinguish a P wave from an S wave from noise.
So this is one of the big advantages of using deep learning.
And then, when it all goes – all this data goes through the model,
the outputs are probability that this input was a P wave
or an S wave or noise. So here’s an example of a P wave
detection in red, with a corresponding high probability during that time,
and S wave detection in blue with a high probability.
And usually a threshold is set on this probability,
and then a phase is picked within that time window.
There’s been a tremendous amount of research in the past couple of years that
have developed these kind of alternative deep learning models for
earthquake detection and phase picking. Here are some of the studies here
listed here. I highlight three examples of studies that have been trained
on the largest data sets – over 700,000 seismograms.
So, on the left, we have PhaseNet, which was trained on northern
California data. In the middle, we have PickNet, which was
trained on data from Japan. On the right, we have EQTransformer,
which was trained on a global data set. And a lot of research has really been
focused on creating new models and architectures, but they’ve all been
trained on very different data sets. And there’s been very limited a
pples-to-apple comparisons between these models.
So that’s something that our group is working on right now.
And, where lately, deep learning models have been applied to later parts of
the monitoring workflow, such as association and location and magnitude,
and seismic networks are now experimenting with adding
deep learning to their operational monitoring workflow.
Some examples are the Oklahoma Geological Survey and the USGS NEIC.
We’re also working on this in the Southern California Seismic Network.
And we have a very ambitious plan a t the Caltech Seismo Lab to completely
rethink how earthquake monitoring is done in a modern way.
So this is the Quakes2AWS project, where AWS is Amazon Web Services.
So this new workflow is going to be cloud-native, even serverless,
so that the program is available on demand.
It’s very scalable in the event of large earthquakes.
This workflow will have a modular architecture so that it’s really easy to
swap different algorithms in and out for testing.
This workflow can easily incorporate latest scientific advances, such as
deep learning models for earthquake detection and phase picking.
And we envision that this workflow can be applied to real-time data,
as well as archive data for research, and eventually in an internet-of-things
sense to all kinds of sensors that can be used for earthquake monitoring,
such as smartphones and smart devices.
So I’ll quickly go through what we’ve done so far in Quakes2AWS.
So here we have data from seismic stations coming into the system.
And waveforms will go into the picker, and then P and S picks go out of the
picker, and then these picks go into the associator, and then events
come out of the associator, and so on. So that’s the overview.
And then here I summarize what Quakes2AWS has implemented to date.
So we have data from the seismic stations coming in real time.
And we actually put it into the existing Earthworm system.
And then the earthquake detection and phase picking is actually done in
the cloud using Amazon Web Services. And then the picks come out of
the cloud. And right now they go into binder, which is
the association step in Earthworm. And then these associated events
will go into the rest of the AQMS/Earthworm software,
so really just isolating the detection and phase-picking steps.
So peeking behind the curtain of the cloud a little bit, we use
Amazon Kinesis to do all the real-time data streaming handling.
And then – so the data goes in real time, and then it’s fed into the deep learning
detector and picker model, and it outputs P and S waves and then picks.
And then these picks will then go out of the cloud and back into Earthworm.
And so what do we plan to do next?
Well, we really want to focus on a rigorous benchmarking and
comparison study to understand how good these deep learning models
really are for earthquake detection and phase picking and association.
How ready are they for operational prime time?
So we want to compare kind of apples- to-apples performance of these models
on a common data set from southern California, both against each other
and also against the operational AQMS/Earthworm software,
both the automatic pics and the manual picks.
And then, in the longer term, we want to continue building out
this cloud-native Quakes2AWS software and set it up as a parallel
development earthquake monitoring system, although this is on hold
until we get more funding for a cloud software architect.
I’d like to summarize that there are many potential operational benefits
of these deep learning modules for earthquake monitoring.
It’s shown to be able to automatically process data with very few errors.
It can pick phases almost as well as humans do.
And then, once trained and tested, we can – it’s easy to apply these
deep learning models in near real time. They’re pretty fast.
And these models have also been shown to perform well when earthquake
sequences get really active, such as aftershocks or swarms.
So here’s an example of, you know, a two-minute long sequence
when the deep learning model was able to many P and S arrivals.
Looking further down the line, deep learning also has a lot of
scientific benefits because it’s really good at finding small
earthquakes that are not in the catalog. So we’re really able to get more
complete earthquake catalogs, which are inputs into improving
forecasting and seismic hazard assessment.
Here’s a magnitude frequency distribution of earthquakes from
the 2020 Puerto Rico sequence that I detected with the
EQTransformer deep learning model. The dark blue shows the catalog
earthquakes it detected, and the light blue shows the
smaller previously unknown earthquakes that were detected –
almost 9 times as many as the catalog earthquakes.
So better picks and more earthquakes detected means
more inputs into tomography, which can improve velocity models
and our understanding of the subsurface.
We can also get more detail about earthquake processes,
such as fine-scale fault structure and fluid migration.
I do want to point out that currently deep learning does have some
limitations before it’s completely ready for earthquake monitoring.
So here’s the same magnitude frequency plot from the Puerto Rico
sequence that I showed in the earlier slide, but now, in red, I’ve added
the earthquakes that the EQTransformer deep learning model
failed to detect but were in the catalog. And you can see that this model
missed some of the larger earthquakes, including the magnitude 6.4
main shock. And that’s definitely a problem for operational monitoring
because, you know, we don’t want to miss out on the largest earthquakes
which are really the most important ones.
And so I’d say that, at this point, deep learning is ready to augment
or add new information to existing earthquake catalogs but not yet ready
to completely replace existing earthquake monitoring systems.
And more research needs to be done to address questions like why these
models fail to detect the larger earthquakes that don’t happen as often.
And also, how can – what can we do to improve these models?
So, to summarize, I’d like to end by saying that machine learning,
and especially deep learning, have a lot of potential to improve
and automate and modernize the way that earthquake monitoring is done.
And there are many operational and scientific benefits that will come out
of using these deep learning models. And so I hope that, in the future,
the path from continuous seismic data to earthquake catalog will go through
our new Quakes2AWS workflow that we’re currently developing.
It really combines a modular cloud-native software architecture
with benchmark deep learning models for every part of the earthquake
monitoring workflow. And here’s my email if you would like to contact me.