SeismoGPT is a transformer autoregressive model achieving median normalized cross-correlation above 0.93 when forecasting synthetic three-component seismograms up to 240 s ahead from P- and S-wave context.
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A shape-aware loss strategy recovers sub-threshold S-wave arrivals in deep learning seismic phase pickers by treating labels as coherent shapes, achieving a 64% increase in effective detections.
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Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss
A shape-aware loss strategy recovers sub-threshold S-wave arrivals in deep learning seismic phase pickers by treating labels as coherent shapes, achieving a 64% increase in effective detections.