Machine learning unearths signature of slow-slip quake origins in seismic data – Science Daily

Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking 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, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.”The machine learning model found that, clos…

Click here to view the original article.