PAW: A Deep Learning Model for Predicting Amplitude Windows in Seismic Signals

Subsurface earthquakes and explosions generate seismic wavefields that are recorded as time-domain signals on sensor networks around the world. To compute key characteristics such as the magnitude of these seismic events, analysts must detect and select the cleanest indicators of seismic phase amplitudes and periods in noisy signals. Existing automated systems designed to pick seismic phase amplitudes and periods require frequent adjustments by human analysts, which becomes a nuisance when the volume of data to process grows large. To address this problem, we have developed a neural network model that accurately replicates the performance of a human analyst 80% of the time and shows potential for decreasing analyst burden by over 40%. We have performed multiple tests on the model and report on its performance compared to existing deep learning techniques.