Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders

Abstract

In this letter, we use deep neural networks for unsupervised clustering of seismic data. We perform the clustering in a feature space that is simultaneously optimized with the clustering assignment, resulting in learned feature representations that are effective for a specific clustering task. To demonstrate the application of this method in seismic signal processing, we design two different neural networks consisting primarily of full convolutional and pooling layers and apply them to: 1) discriminate waveforms recorded at different hypocentral distances and 2) discriminate waveforms with different first-motion polarities. Our method results in precisions that are comparable to those recently achieved by supervised methods, but without the need for labeled data, manual feature engineering, and large training sets. The applications we present here can be used in standard single-site earthquake early warning systems to reduce the false alerts on an individual station level. However, the presented technique is general and suitable for a variety of applications including quality control of the labeling and classification results of other supervised methods.