Locating earthquakes with a network of seismic stations via a deep learning method

TL;DR

This paper presents a deep learning approach using a convolutional neural network to accurately locate earthquakes in real time across large areas with minimal computational effort, improving seismic monitoring capabilities.

Contribution

It introduces a fully convolutional network trained on historic data to locate earthquakes in 3D space rapidly without requiring velocity models or human input.

Findings

Achieved an average error of 4.9 km in epicenter location.

Located 194 earthquakes with high speed and accuracy.

Operates in approximately 0.01 seconds per event.

Abstract

The accurate and automated determination of earthquake locations is still a challenging endeavor. However, such information is critical for monitoring seismic activity and assessing potential hazards in real time. Recently, a convolutional neural network was applied to detect earthquakes from single-station waveforms and approximately map events across several large surface areas. In this study, we locate 194 earthquakes induced during oil and gas operations in Oklahoma, USA, within an error range of approximately 4.9 km on average to the epicenter and 1.0 km to the depth in catalogs with data from 30 network stations by applying the fully convolutional network. The network is trained by 1,013 historic events, and the output is a 3D volume of the event location probability in the Earth. The trained system requires approximately one hundredth of a second to locate an event without the need for any velocity model or human interference.