Wenhuan Kuang, Congcong Yuan, Zhihui Zou, Jie Zhang, Wei Zhang
{"title":"Autonomous Earthquake Location via Deep Reinforcement Learning","authors":"Wenhuan Kuang, Congcong Yuan, Zhihui Zou, Jie Zhang, Wei Zhang","doi":"10.1785/0220230118","DOIUrl":null,"url":null,"abstract":"Abstract Recent advances in artificial intelligence allow seismologists to upgrade the workflow for locating earthquakes. The standard workflow concatenates a sequence of data processing modules, including event detection, phase picking, association, and event location, with elaborately fine-tuned parameters, lacking automation and convenience. Here, we leverage deep reinforcement learning and develop a state-of-the-art earthquake robot (EQBot) to help advance automated earthquake location. The EQBot learns from tremendous trial-and-error explorations, which aims to best align the observed P and S waves, complying with the geophysical principle of gather alignments in source imaging. After training on earthquakes (M ≥ 2.0) for a decade in the Los Angeles region, it can locate earthquakes directly from waveforms with mean absolute errors of 1.32 km, 1.35 km, and 1.96 km in latitude, longitude, and depth, respectively, closely comparable to the cataloged locations. Moreover, it can automatically implement quality control by examining the alignments of P and S waves. Our study provides a new solution to advance the earthquake location process toward full automation.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"33 2 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seismological Research Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1785/0220230118","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Recent advances in artificial intelligence allow seismologists to upgrade the workflow for locating earthquakes. The standard workflow concatenates a sequence of data processing modules, including event detection, phase picking, association, and event location, with elaborately fine-tuned parameters, lacking automation and convenience. Here, we leverage deep reinforcement learning and develop a state-of-the-art earthquake robot (EQBot) to help advance automated earthquake location. The EQBot learns from tremendous trial-and-error explorations, which aims to best align the observed P and S waves, complying with the geophysical principle of gather alignments in source imaging. After training on earthquakes (M ≥ 2.0) for a decade in the Los Angeles region, it can locate earthquakes directly from waveforms with mean absolute errors of 1.32 km, 1.35 km, and 1.96 km in latitude, longitude, and depth, respectively, closely comparable to the cataloged locations. Moreover, it can automatically implement quality control by examining the alignments of P and S waves. Our study provides a new solution to advance the earthquake location process toward full automation.