Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event-type classification, particularly discrimination of single-fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P_prob − S_prob, as a discriminant. Test data include M_L ∼ 1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P_prob − S_prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P_prob − S_prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.