New technology from Stanford scientists finds
long-hidden quakes, and possible clues about
how earthquakes evolve.
New technology from Stanford scientists finds long-hidden quakes,
and possible clues about how earthquakes evolve.
Tiny movements in Earth’s outermost layer may provide a Rosetta
Stone for deciphering the physics and warning signs of big quakes.
New algorithms that work a little like human vision are now detecting
these long-hidden microquakes in the growing mountain of seismic data.
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.
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
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
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.
Earthquakes detected and located by Earthquake Transformer in the Tottori
area. (Credit: Mousavi et al., 2020 Nature Communications):
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.”
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System