More advances moving towards closer to rea-time prediction?
Technology Finds Long-Hidden Quakes, Possible Clues About How Earthquakes Evolve
Stanford News, Josie Garthwaite
Stanford University scientists developed new algorithms that extract evidence of long-hidden microquakes from massive seismic datasets. The Earthquake Transformer algorithm emulates how human analysts holistically analyze a set of seismic "wiggles," then focuses on a small section of interest. The Stanford team measured the algorithm’s performance using five weeks of data recorded in the region of Japan impacted two decades ago by the Tottori earthquake and its aftershocks. The algorithm detected 21,092 events—more than 2.5 times the number of quakes detected manually—within 20 minutes, using data from just 18 of 57 stations originally used to study the sequence. Said Stanford’s Gregory Beroza, “Earthquake monitoring using machine learning in near-real time is coming very soon.”
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