Science Daily October 22, 2020
Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes. A team of researchers (Stanford University, Georgia Institute of Technology) has developed a global deep-learning model for simultaneous earthquake detection and phase picking. Performing these two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using a hierarchical attention mechanism. They applied their model to 5 weeks of continuous data recorded during 2000 Tottori earthquakes in Japan and detected and located two times more earthquakes using only a portion (less than 1/3) of seismic stations. Their model outperforms previous deep-learning and traditional phase-picking and detection algorithms…read more. Open Access TECHNICAL ARTICLE
Network architecture. Credit: Nature Communications volume 11, Article number: 3952 (2020)