Machine learning finds quake origin signatures – IAM Network

Combing through historical seismic data with a machine learning model, US researchers have unearthed distinct statistical features that marked the formative stage of slow-slip ruptures in the Earth's crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, they suggest, in a paper in the journal Nature Communications, that these distinct signatures may help geophysicists understand the timing of the faster quakes as well. "The… model found that, close to the end of the slow slip cycle, a snapshot of the data is imprinted with fundamental information regarding the upcoming failure of the system," says lead author Claudia Hulbert, from the Los Alamos National Laboratory. "Our results suggest that slow-slip rupture may well be predictable, and because slow slip events have a lot in common with earthquakes, slow-slip events may provide an easier way to study the fundamental physics of earth rupture."