Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method.

ABSTRACT: The accurate and automated determination of small earthquake (M_L < 3.0) locations is still a challenging endeavor due to low signal-to-noise ratio in data. However, such information is critical for monitoring seismic activity and assessing potential hazards. In particular, earthquakes caused by industrial injection have become a public concern, and regulators need a solid capability for estimating small earthquakes that may trigger the action requirements for operators to follow in real time. In this study, we develop a fully convolutional network and locate earthquakes induced during oil and gas operations in Oklahoma with data from 30 network stations. The network is trained by 1,013 cataloged events (M_L ≥ 3.0) as base data along with augmented data accounting for s

SUBMITTER: Zhang X 

PROVIDER: S-EPMC7005003 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature