Convolutional Neural Network for Earthquake Detection and Location

2017/02/07 by Thibaut Perol, Michaël Gharbi, Perol, Thibaut +3 · 2 citations
Computer Science · Earth and Planetary Sciences · #Earthquake Detection and Analysis #FOS: Physical sciences #Geophysics (physics.geo-ph) #Seismic Waves and Analysis #Seismology and Earthquake Studies

paper · pdf · doi:10.48550/arxiv.1702.02073

openalex publication_date 2017/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Exploration of Machine Learning Methods to Seismic Event Discrimination in the Pacific Northwest
• Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency

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