From Knowable Magazine, October 22:
Machine learning is expanding scientists’ catalogs of quakes and refining maps of underground faults. It also promises to improve quake forecasts.
When the biggest earthquake in more than a decade rattled Russia’s remote Kamchatka Peninsula in July, seismologists around the world knew within moments. For earthquakes big or small, sensors around the globe detect the tremors and relay that information to researchers, who quickly analyze the observations and issue alerts.
Now artificial intelligence is poised to make almost everything about earthquake research much faster — and to rewrite researchers’ very understanding of how earthquakes happen.
By using a subfield of AI called machine learning, some scientists are identifying up to millions of tiny, previously unseen earthquakes in data gathered from seismically active places. These new and improved databases are helping researchers to better understand the geological faults along which quakes happen, and can help to illuminate the risks of future quakes. Some scientists are even using machine learning to improve their forecasts of how many aftershocks may rattle a location that has just experienced a large and damaging earthquake.
More broadly, researchers hope that machine learning, with its ability to crunch through huge amounts of information and learn from the patterns within, will reveal fresh insights into some of the biggest mysteries about earthquakes, including how a quake unfolds in its first devastating seconds.
“Machine learning opened a whole new window,” says Mostafa Mousavi, a seismologist at Harvard University.
Shaking earth, exploding data
Earthquakes happen when geological stress builds up in the ground, such as when two plates of Earth’s crust grind alongside one another, as they do at California’s San Andreas Fault. At some point, the stress reaches a critical threshold and the fault ruptures, breaking the rock and causing seismic energy to ripple outward and shake the ground.That energy is recorded by seismometers and other instruments around the world, which are positioned in great numbers in geologically active areas like California and Japan. The data feed into national and international systems for tracking earthquakes and alerting the world. The amount of data has exploded in recent years as seismologists find new ways to gather information on ground movements — like detecting seismic signals over fiber optic networks, or using the accelerometers built into smartphones to create a phone-based earthquake warning network.
Just a decade or two ago, much of the analysis of seismic signals was done by hand, with scientists working as quickly as possible to assess recordings coming in from their observing networks. But today, there are just too many data points. “Now the only — almost — way that you can deal with the seismic data is to go to automatic processing,” says Mousavi, who coauthored a 2023 article in the Annual Review of Earth and Planetary Sciences on machine learning in earthquake seismology.
One of the most common uses of machine learning in seismology is measuring the arrival time of seismic waves at a particular location, a process known as phase picking. Earthquakes generate two kinds of seismic waves, known as P and S waves, that affect the ground in different ways and show up as different types of squiggles on a seismogram. In the past, a seismologist would analyze data arriving from seismic sensors and hand-select what they gauged to be the start of P waves or S waves on those seismogram plots. Picking the starts of those waves accurately and precisely is important for understanding factors such as where exactly the earthquake hit. But phase picking is very time consuming....
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