AI Revolutionizes Earthquake Detection, Uncovering Previously Unnoticed Seismic Events
Artificial intelligence is dramatically changing the field of seismology, enabling the detection of earthquakes previously too small to be noticed, including a magnitude -0.53 event recorded in Calipatria, California, on January 1, 2008.
Over the past seven years, machine-learning tools have largely automated earthquake detection, a task historically performed by human analysts. These AI systems can identify smaller seismic events, particularly in areas with significant background noise like urban environments. This increased sensitivity provides valuable data for understanding Earth’s composition and potential future hazards – crucial for regions prone to seismic activity like those along the US Geological Survey’s earthquake hazard maps.
“In the best-case scenario, when you adopt these new techniques, even on the same old data, it’s kind of like putting on glasses for the first time, and you can see the leaves on the trees,” explained Kyle Bradley, co-author of the Earthquake Insights newsletter. Judith Hubbard, a professor at Cornell University and Bradley’s co-author, added, “It’s really remarkable.” The ability of AI to process seismic waves, similar to how sound waves travel through air, allows for more detailed inferences about the materials they pass through. You can learn more about seismogram basics from the Incorporated Research Institutions for Seismology.
While AI has revolutionized earthquake detection, scientists believe the full potential of these technologies – including improved earthquake forecasting – remains untapped. As of today, October 10, 2025, researchers are continuing to explore how machine learning can be applied to other data processing tasks within seismology.
Experts say the revolution in earthquake science is ongoing, and further advancements are expected in the coming years.
On January 1, 2008, at 1:59 am in Calipatria, California, an earthquake happened. You haven’t heard of this earthquake; even if you had been living in Calipatria, you wouldn’t have felt anything. It was magnitude -0.53, about the same amount of shaking as a truck passing by. Still, this earthquake is notable, not because it was large but because it was small—and yet we know about it.
Over the past seven years, AI tools based on computer imaging have almost completely automated one of the fundamental tasks of seismology: detecting earthquakes. What used to be the task of human analysts—and later, simpler computer programs—can now be done automatically and quickly by machine-learning tools.
These machine-learning tools can detect smaller earthquakes than human analysts, especially in noisy environments like cities. Earthquakes give valuable information about the composition of the Earth and what hazards might occur in the future.
“In the best-case scenario, when you adopt these new techniques, even on the same old data, it’s kind of like putting on glasses for the first time, and you can see the leaves on the trees,” said Kyle Bradley, co-author of the Earthquake Insights newsletter.
I talked with several earthquake scientists, and they all agreed that machine-learning methods have replaced humans for the better in these specific tasks.
“It’s really remarkable,” Judith Hubbard, a Cornell University professor and Bradley’s co-author, told me.
Less certain is what comes next. Earthquake detection is a fundamental part of seismology, but there are many other data processing tasks that have yet to be disrupted. The biggest potential impacts, all the way to earthquake forecasting, haven’t materialized yet.
“It really was a revolution,” said Joe Byrnes, a professor at the University of Texas at Dallas. “But the revolution is ongoing.”
When an earthquake happens in one place, the shaking passes through the ground, similar to how sound waves pass through the air. In both cases, it’s possible to draw inferences about the materials the waves pass through.
