GPU-Accelerated Automatic Microseismic Monitoring Algorithm (GAMMA) and Its Application to the 2019 Ridgecrest Earthquake Sequence

Abstract

Abstract Foreshocks and/or aftershocks play critical roles in improving our understanding of the processes of faulting, such as nucleation of earthquakes, earthquake triggering, and postseismic deformation. A rapid and accurate earthquake detection and location algorithm can provide timely information of seismic activities, thereby benefitting our understanding of physical mechanisms of faulting and seismic hazard assessment. We have developed a graphic processing unit (GPU)-accelerated automatic microseismic monitoring algorithm (GAMMA) for accurate and near real-time detection and location of earthquakes. GAMMA utilizes methods based on backprojection to automatically detect potential earthquakes, and then the waveforms of qualified earthquakes are selected as templates when searching for small earthquakes in continuous recordings using the template-matching algorithm. The use of GPUs has substantially accelerated the calculations and has made GAMMA capable of (near-)real-time earthquake monitoring. We have successfully applied GAMMA to the 2019 Ridgecrest earthquake sequence in southern California. The number of earthquakes detected by GAMMA is more than 21 times that documented in the regional catalog. The more complete catalog determined by GAMMA may provide crucial information for improving our understanding of the physical mechanisms of faulting and also supply useful constraints for a variety of types of studies, including dynamic rupture simulations and crustal deformation modeling.