Science
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 tec…
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…
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