HD-TMA: A New Fast Template Matching Algorithm Implementation for Linear DAS Array Data and Its Optimization Strategies

Abstract

Abstract Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber-optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends on heavy computational cost and diverse template events. We developed a program named HD-TMA (high-efficiency DAS template matching algorithm), which accelerates computation by 40 times on the central processing unit platform and 2 times on the graphic processing unit platform. For linear DAS array data, we introduced a fast arrival-picking algorithm based on the Hough transform to pick the time window of template waveform. The HD-TMA was successfully applied to the 2022 Ms 6.9 Menyuan earthquake aftershock sequence recorded by a DAS array, and the DAS data result was compared with a collocated short-period seismometer data’s result. Two optimization strategies were discussed based on this data set. (1) Using signal-to-noise ratio in choosing the location and aperture of the subarray and the time window of the template waveform. (2) Considering the decrease in template events’ marginal utility, we proposed applying a neural network to build a template event library, followed by the HD-TMA scanning. Such strategies can effectively reduce computational cost and improve detection capability.