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AI detects hidden earthquakes – Gadget

Measures of Earth’s vibrations zigged and zagged across Mostafa
Mousavi’s screen one morning in Memphis, Tenn. As part of his PhD
studies in geophysics, he sat scanning earthquake signals recorded the
night before, verifying that decades-old algorithms had detected true
earthquakes rather than tremors generated by ordinary things like
crashing waves, passing trucks or stomping football fans.

“I did all this tedious work for six months, looking at continuous data,” says Mousavi, now a research scientist at Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth). “That was the point I thought, ‘There has to be a much better way to do this stuff.’”

This was in 2013. Smartphones were already loaded with algorithms that could break down speech into sound waves and come up with the most likely words in those patterns. Using artificial intelligence, they could even learn from past recordings to become more accurate over time.

Seismic
waves and sound waves aren’t so different. One moves through rock and
fluid, the other through air. Yet while machine learning had transformed
the way personal computers process and interact with voice and sound,
the algorithms used to detect earthquakes in streams of seismic data
have hardly changed since the 1980s.

That has left a lot of earthquakes undetected.

Big
quakes are hard to miss, but they’re rare. Meanwhile, imperceptibly
small quakes happen all the time. Occurring on the same faults as bigger
earthquakes – and involving the same physics and the same mechanisms –
these “microquakes” represent a cache of untapped information about how
earthquakes evolve – but only if scientists can find them.

Newswise — In a recent paper published in Nature Communications,
Mousavi and co-authors describe a new method for using artificial
intelligence to bring into focus millions of these subtle shifts of the
Earth. “By improving our ability to detect and locate these very small
earthquakes, we can get a clearer view of how earthquakes interact or
spread out along the fault, how they get started, even how they stop,”
said Stanford geophysicist Gregory Beroza, one of the paper’s authors.

Focusing on what matters

Mousavi
began working on technology to automate earthquake detection soon after
his stint examining daily seismograms in Memphis, but his models
struggled to tune out the noise inherent to seismic data. A few years
later, after joining Beroza’s lab at Stanford in 2017, he started to
think about how to solve this problem using machine learning.

The
group has produced a series of increasingly powerful detectors. A 2018
model called PhaseNet, developed by Beroza and graduate student Weiqiang
Zhu, adapted algorithms from medical image processing to excel at
phase-picking, which involves identifying the precise start of two
different types of seismic waves. Another machine learning model,
released in 2019 and dubbed CRED, was inspired by voice-trigger algorithms in virtual assistant systems and
proved effective at detection. Both models learned the fundamental
patterns of earthquake sequences from a relatively small set of
seismograms recorded only in northern California.

In the Nature Communications paper,
the authors report they’ve developed a new model to detect very small
earthquakes with weak signals that current methods usually overlook, and
to pick out the precise timing of the seismic phases using earthquake
data from around the world. They call it Earthquake Transformer.

According to Mousavi: “The model builds on PhaseNet and CRED, and embeds those insights I got from the time I was doing all of this manually.” Specifically, Earthquake Transformer mimics the way human analysts look at the set of wiggles as a whole and then hone in on a small section of interest.

People do this intuitively in daily life – tuning out
less important details to focus more intently on what matters. Computer
scientists call it an “attention mechanism” and frequently use it to
improve text translations. But it’s new to the field of automated
earthquake detection, Mousavi said. “I envision that this new generation
of detectors and phase-pickers will be the norm for earthquake
monitoring within the next year or two,” he said.

The technology could allow analysts to focus on extracting insights from a more complete catalog of earthquakes, freeing up their time to think more about what the pattern of earthquakes means, said Beroza, the Wayne Loel Professor of Earth Science at Stanford Earth.

Hidden faults

Understanding
patterns in the accumulation of small tremors over decades or centuries
could be key to minimizing surprises – and damage – when a larger quake
strikes.

The 1989 Loma Prieta quake ranks as one of the most destructive earthquake disasters in
U.S. history, and as one of the largest to hit northern California in
the past century. It’s a distinction that speaks less to extraordinary
power in the case of Loma Prieta than to gaps in earthquake
preparedness, hazard mapping and building codes – and to the extreme
rarity of large earthquakes.

Only about one in five of the approximately 500,000 earthquakes detected globally by seismic sensors every year produce shaking strong enough for people to notice. In a typical year, perhaps 100 quakes will cause damage.

Earthquakes detected and located by EarthquakeTransformer in the Tottori area.

In the
late 1980s, computers were already at work analyzing digitally recorded
seismic data, and they determined the occurrence and location of
earthquakes like Loma Prieta within minutes. Limitations in both the
computers and the waveform data, however, left many small earthquakes
undetected and many larger earthquakes only partially measured.

After the harsh lesson of Loma Prieta, many California communities have come to rely on maps showing
fault zones and the areas where quakes are likely to do the most
damage. Fleshing out the record of past earthquakes with Earthquake
Transformer and other tools could make those maps more accurate and help
to reveal faults that might otherwise come to light only in the wake of destruction from
a larger quake, as happened with Loma Prieta in 1989, and with the
magnitude-6.7 Northridge earthquake in Los Angeles five years later.

“The more information we can get on the deep, three-dimensional fault structure through improved monitoring of small earthquakes, the better we can anticipate earthquakes that lurk in the future,” Beroza says.

Earthquake Transformer

To
determine an earthquake’s location and magnitude, existing algorithms
and human experts alike look for the arrival time of two types of waves.
The first set, known as primary or P waves, advance quickly – pushing,
pulling and compressing the ground like a Slinky as they move through
it. Next come shear or S waves, which travel more slowly but can be more
destructive as they move the Earth side to side or up and down.

To
test Earthquake Transformer, the team wanted to see how it worked with
earthquakes not included in training data that are used to teach the
algorithms what a true earthquake and its seismic phases look like. The
training data included one million hand-labeled seismograms recorded
mostly over the past two decades where earthquakes happen globally,
excluding Japan. For the test, they selected five weeks of continuous
data recorded in the region of Japan shaken 20 years ago by the
magnitude-6.6 Tottori earthquake and its aftershocks.

The model
detected and located 21,092 events – more than two and a half times the
number of earthquakes picked out by hand, using data from only 18 of the
57 stations that Japanese scientists originally used to study the
sequence. Earthquake Transformer proved particularly effective for the
tiny earthquakes that are harder for humans to pick out and being
recorded in overwhelming numbers as seismic sensors multiply.

“Previously,
people had designed algorithms to say, find the P wave. That’s a
relatively simple problem,” explained co-author William Ellsworth, a
research professor in geophysics at Stanford. Pinpointing the start of
the S wave is more difficult, he said, because it emerges from the
erratic last gasps of the fast-moving P waves. Other algorithms have
been able to produce extremely detailed earthquake
catalogs, including huge numbers of small earthquakes missed by
analysts – but their pattern-matching algorithms work only in the region
supplying the training data.

With Earthquake Transformer running
on a simple computer, analysis that would ordinarily take months of
expert labor was completed within 20 minutes. That speed is made
possible by algorithms that search for the existence of an earthquake
and the timing of the seismic phases in tandem, using information
gleaned from each search to narrow down the solution for the others.

“Earthquake
Transformer gets many more earthquakes than other methods, whether it’s
people sitting and trying to analyze things by looking at the
waveforms, or older computer methods,” Ellsworth said. “We’re getting a
much deeper look at the earthquake process, and we’re doing it more
efficiently and accurately.”

The researchers trained and tested
Earthquake Transformer on historic data, but the technology is ready to
flag tiny earthquakes almost as soon as they happen. According to
Beroza, “Earthquake monitoring using machine learning in near real-time
is coming very soon.”


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