Convolutional Neural Network for Earthquake Detection and Location

TL;DR

This paper introduces ConvNetQuake, a convolutional neural network that efficiently detects and locates earthquakes from single waveforms, significantly increasing detection rates and speed compared to traditional methods.

Contribution

The paper presents a novel AI-based approach for earthquake detection and location that outperforms existing methods in speed and detection capability.

Findings

Detected 20 times more earthquakes than previous catalogs.

Algorithm is orders of magnitude faster than traditional methods.

Successfully applied to induced seismicity in Oklahoma.

Abstract

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. In this work, 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 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods.