Focal mechanism determination by location-constrained deep learning: application to microseismic monitoring

Abstract

Accurate and rapid determination of focal mechanism solutions is of great significance for real-time seismic monitoring. Previous deep learning approaches for focal mechanism determination typically use the waveform data, which is sensitive to the velocity model and inherently includes information about both the source location and the focal mechanism. We introduce a location-constrained deep learning algorithm for determining the focal mechanism for surface microseismic events. By using aligned P-wave data along with azimuth and take-off angle as input, we narrow the solution space for the focal mechanism problem and reduce the dependence on the velocity model. The model is trained using 40,000 theoretical samples generated with the geometry and velocity model of the field data. Validation tests, comparisons with a waveform-based network, velocity perturbation tests, and location error tests are performed to demonstrate the robustness and efficiency of our method. After applying the trained model to field data, the results demonstrate that our method is fast and achieves comparable accuracy to HASH results for high-quality events, making it promising for real-time microseismic monitoring.