RECOVAR: Representation Covariances on Deep Latent Spaces for Seismic Event Detection

TL;DR

This paper introduces RECOVAR, an unsupervised deep learning method using autoencoders and covariance-based triggers for earthquake detection, achieving comparable or better results than supervised methods and demonstrating strong cross-dataset generalization.

Contribution

RECOVAR is a novel unsupervised approach that detects earthquakes from raw waveforms without labeled data, improving generalization across datasets.

Findings

Performance comparable to supervised methods

Strong cross-dataset generalization

Effective in detecting earthquakes from raw waveforms

Abstract

While modern deep learning methods have shown great promise in the problem of earthquake detection, the most successful methods so far have been based on supervised learning, which requires large datasets with ground-truth labels. The curation of such datasets is both time consuming and prone to systematic biases, which result in difficulties with cross-dataset generalization, hindering general applicability. In this paper, we develop an unsupervised method for earthquake detection that learns to detect earthquakes from raw waveforms, without access to ground truth labels. The performance is comparable to, and in some cases better than, some state-of-the-art supervised methods. Moreover, the method has strong \emph{cross-dataset generalization} performance. The algorithm utilizes deep autoencoders that learn to reproduce the waveforms after a data-compressive bottleneck and uses a simple, cross-covariance-based triggering algorithm at the bottleneck for labeling. The approach has the potential to be useful for time series datasets from other domains.