RECOVAR: Representation Covariances on Deep Latent Spaces for Seismic Event Detection
This addresses the problem of earthquake detection for seismologists by reducing reliance on labeled data, though it is incremental as it builds on existing autoencoder techniques.
The paper tackles earthquake detection by developing an unsupervised method using deep autoencoders and cross-covariance triggering, achieving performance comparable to or better than some state-of-the-art supervised methods with strong cross-dataset generalization.
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.