Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification

TL;DR

This paper presents two deep learning models, ResNet and multi-branch ResNet, that improve the detection and classification of small magnitude earthquakes and seismic phases in noisy data, outperforming previous methods.

Contribution

The study introduces novel deep learning frameworks for seismic detection and phase classification, demonstrating improved robustness and accuracy over existing approaches.

Findings

Achieved 4% improvement in earthquake detection accuracy.

Enhanced seismic phase classification accuracy.

Models perform well even with low signal-to-noise ratio.

Abstract

Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data is witnessed. This makes the handling of the seismic data rather daunting based on traditional approaches and therefore fuels the need for a more robust and reliable method. In this study, we investigate two deep learningbased models, termed 1D ResidualNeuralNetwork (ResNet) and multi-branch ResNet, for tackling the problem of seismic signal detection and phase identification, especially the later can be used in the case where multiple classes is organized in the hierarchical format. These methods are trained and tested on the dataset of the Southern California Seismic Network. Results demonstrate that the proposed methods can achieve robust performance for the detection of seismic signals, and the identification of seismic phases, even when the seismic events are of small magnitude and are masked by noise. Compared with previously proposed deep learning methods, the introduced frameworks achieve 4% improvement in earthquake monitoring, and a slight enhancement in seismic phase classification.