Convolutional neural network for earthquake detection and location

作者：Thibaut Perol, Michaël Gharbi, Marine Denolle · 发表于：Science Advances · 年份：2018 · DOI：10.1126/sciadv.1700578 · 被引用次数：943 · 研究领域：Seismology and Earthquake Studies、Earthquake Detection and Analysis、Seismic Waves and Analysis

The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods.