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

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.

Application of Waveform Stacking Methods for Seismic Location at Multiple Scales

“…Another challenge for stacking-based location methods is the relatively high computation cost due to waveform storage and transmission, especially for real-time applications with large monitoring arrays. High-performance computing facilities [52,53] and advanced inversion algorithms [35,39] are promising solutions for improving the computational efficiency.…”

RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning

“…Here, the P and S phase-time functions label the P and S arrivals along X, and the earthquake waveform mask wraps the P-S pair of the same waveform. We considered the earthquake waveform mask as an additional constraint to facilitate the process of earthquake location [34], [35].…”