DV4.3 Machine learning approaches for earthquake detection

This report is a review of machine learning-based methods for earthquake detection. It first introduces the basic concepts of traditional earthquake detection, and commonly used approaches. It then goes onto describe the concept of artificial intelligence, machine learning, and deep learning. This i...

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Bibliographic Details
Published in:NGI-rapport 2024
Main Authors: Kettlety, Tom, Abraha, Tesfahiwet, Kendall, J. Michael
Format: Report
Language:English
Subjects:
Deep Learning
Earthquake Detection
Machine Learning
Phase Picking
Seismology
Online Access:Request full text
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Summary:This report is a review of machine learning-based methods for earthquake detection. It first introduces the basic concepts of traditional earthquake detection, and commonly used approaches. It then goes onto describe the concept of artificial intelligence, machine learning, and deep learning. This is followed with a summary of how these concepts are employed to detect earthquakes in seismic data, with a synthesis of the most widely used machine learning detection algorithms. The ways in which machine learning methods could be applied to the activities in the SHARP project are given in the conclusions, along with the future directions of the field.