Strong earthquakes are hard to miss, but they are rare. Meanwhile, they pass unnoticed
If we improve our ability to detect andlocate these very small earthquakes, we can get a clearer picture of how earthquakes interact or propagate along a fault, how they start, and even how they stop.
Gregory Beroza, a Stanford physicist and one of the authors of the paper
Mousavi started working on technology forearthquake detection automation. Previously, he studied daily seismograms in Memphis. A few years later, after joining Beroza's lab at Stanford, he began to think about how to solve the problem of detecting cortical vibrations using machine learning.
In their paper, the authors explain that they have developed a new model for detecting very small earthquakes with weak signals. They call it an earthquake transformer.
According to Mousavi, the model is built on PhaseNet andCRED and "includes those algorithms that scientists make by hand." Specifically, Earthquake Transformer mimics how human analysts look at a set of movements as a whole, and then hone in on a small piece of interest.
This technology can enable analystsfocus on extracting information from a more complete catalog of earthquakes. The new algorithm saves researchers' time, which they are going to spend on analyzing the nature of the earthquake.
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