Using sparse data to predict lab quakes
A machine-learning approach developed for sparse data reliably predicts fault slip in laboratory earthquakes and could be key to predicting fault slip and potentially earthquakes in the field. The research …
Machine learning refines earthquake detection capabilities
Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new …
Taking Yellowstone seismology to the classroom for some “deep learning”
While the automated monitoring system currently in place for detecting and processing earthquakes in Yellowstone works quite well most of the time, its solutions need to be reviewed and refined …
Machine learning and earthquake risk prediction
Homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid — as sometimes happens during earthquakes — it can topple buildings …
Berkeley Lab’s Advanced Monitoring Capabilities Still in Use 10 Years After Fukushima Earthquake and Nuclear Power Plant Disaster
Kai Vetter, a UC Berkeley nuclear engineering professor and head of the Applied Nuclear Physics program at Lawrence Berkeley National Laboratory (Berkeley Lab), recalled the rush of reporters as he …