Exploring the Manifold of Seismic Waves: Application to the Estimation of Arrival-Times

TL;DR

This paper introduces a novel nonlinear manifold learning approach for seismic wave analysis that improves the accuracy of arrival-time estimation over traditional methods, validated on real seismic datasets.

Contribution

The paper presents a new method combining time-delay embedding and graph Laplacian eigenvectors for seismic wave analysis, demonstrating superior performance.

Findings

Outperforms traditional analysis methods like SSA, wavelet analysis, and STA/LTA.

Validated on seismic data from Idaho, Montana, Wyoming, and Utah (2005-2006).

Provides more accurate seismic wave arrival-time estimates.

Abstract

We propose a new method to analyze seismic time series and estimate the arrival-times of seismic waves. Our approach combines two ingredients: the times series are first lifted into a high-dimensional space using time-delay embedding; the resulting phase space is then parametrized using a nonlinear method based on the eigenvectors of the graph Laplacian. We validate our approach using a dataset of seismic events that occurred in Idaho, Montana, Wyoming, and Utah, between 2005 and 2006. Our approach outperforms methods based on singular-spectrum analysis, waveleta nalysis, and STA/LTA.