Los Alamos National Laboratory: Machine Learning Unearths Signature of Slow-slip Quake Origins in Seismic Data
August 19, 2020
August 19, 2020
WASHINGTON, Aug. 19 -- The U.S. Department of Energy's Los Alamos National Laboratory issued the following news release on Aug. 18:
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, t . . .
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, t . . .