Unsupervised machine learning and k-Means clustering as a way of discovering anomalous events In continuous seismic time series

Abstract

Unsupervised k-Means clustering was implemented as a method for identifying anomalies in seismic time series. Sliding window approach was used for generating specific subsequences from the overall waveform. Dynamic Time Warping (DTW) was used as the method for comparing seismic subsequences. DTW barycenter averaging (DBA) was used as the method for averaging multiple subsequences within a group of similiar shapes. Clustering is able to discover anomalously shaped parts of a seismic time series in a completely unsupervised fashion, without requiring anyone to input actual times of the events, any predetermiend examples of events, or any other parameters about the signal.