Artificial intelligence learns to detect weak earthquakes and cortical shifts

A new artificial intelligence has emerged, which can predict not only large earthquakes, but also small shifts in the Earth’s crust.

Strong earthquakes are difficult to miss, but rare. Meanwhile, small transformations in the cortex pass imperceptibly, which occur constantly. They occur on the same faults as strong earthquakes, and share the same mechanisms. These “small earthquakes” are fertile ground for research into the development of changes in the Earth’s crust.

If we improve our ability to detect and identify these very small earthquakes, we can get a clearer idea of ​​how earthquakes interact or propagate along the fault, how they occur, and even how they end.

Gregory Piroza, a geophysicist at Stanford University and one of the article’s authors, has begun work on the technology to automate the detection of earthquakes. He previously studied daily gatherings in Memphis. A few years later, after joining the Perosa 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 earthquake transformer.

According to Mousavi, the model is built on PhaseNet and CRED and “includes algorithms that scientists create manually.” In particular, Earthquake Transformer simulates the way human analysts view a set of movements as a whole and then focus on a small fraction of the interest.

This technology could allow analysts to focus on extracting information from a more complete catalog of earthquakes. The new algorithm saves the researcher’s time that they would spend analyzing the nature of an earthquake.

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