Shapelets for earthquake detection

TL;DR

This paper presents EQShapelets, a shape-based machine learning method for earthquake detection that is noise-robust, magnitude-independent, and offers interpretability, outperforming existing algorithms in detection accuracy and providing valuable insights.

Contribution

Introduction of EQShapelets, a novel shape-based approach for earthquake detection that is noise-robust, magnitude-independent, and enhances interpretability in seismic analysis.

Findings

Detected all cataloged earthquakes in tested data

Identified 281 uncataloged events with lower false detection rate

Outperformed autocorrelation and FAST algorithms in detection accuracy

Abstract

This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome the challenges in the field of seismology related to automated detection and cataloging of earthquakes. EQShapelets are amplitude and phase-independent, i.e., their detection sensitivity is irrespective of the magnitude of the earthquake and the time of occurrence. They are also robust to noise and other spurious signals. The detection capability of EQShapelets is tested on one week of continuous seismic data provided by the Northern California Seismic Network (NCSN) obtained from a station in central California near the Calaveras Fault. EQShapelets combined with a Random Forest classifier, detected all of the cataloged earthquakes and 281 uncataloged events with lower false detection rate thus offering a better performance than autocorrelation and FAST algorithms. The primary advantage of EQShapelets over competing methods is the interpretability and insight it offers. Shape-based approaches are intuitive, visually meaningful and offers immediate insight into the problem domain that goes beyond their use in accurate detection. EQShapelets, if implemented at a large scale, can significantly reduce catalog completeness magnitudes and can serve as an effective tool for near real-time earthquake monitoring and cataloging.