Graphics Processing Unit-Based Match and Locate (GPU-M&L): An Improved Match and Locate Method and Its Application

Abstract

Abstract Microearthquake detection and location are critical for understanding earthquake mechanisms and mitigating seismic hazards. Match and locate (M&L) is an effective method for simultaneously detecting and locating small earthquakes. However, the heavy computational demands of the M&L make it challenging to apply to big data. In this article, we develop an improved M&L method—called graphics processing unit-based M&L (GPU-M&L). The GPU-M&L differs from the M&L in two ways: (1) adding weighting factor for each component of templates to improve the detection ability and (2) implementing the M&L method on GPU to accelerate the computation. Synthetic tests show the GPU-M&L can not only handle smaller earthquakes than the M&L but also perform 4.5 times faster than the M&L parallelly programed on central processing unit. As an example, we utilize the GPU-M&L to study the seismic activity during seven days after the 2015 Ms 5.8 Alxa, China, earthquake (from 15 to 21 April 2015). Using 38 cataloged earthquakes as templates, we detect ∼20 times more events than in the routine catalog. The distribution of those detected events, along with focal mechanisms of large events, suggests that the 2015 Ms 5.8 earthquake occurred on an east–west-trending hidden strike-slip fault.