Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity
Abstract Similarity search is a popular technique for seismic signal processing, with template matching, matched filters, and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross‐correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope’s USArray experiment, a path‐rich dataset of 207,291 regional seismograms across 8452 unique events is constructed, and then employed via the batch‐hard triplet loss function, to train a deep convolutional neural network that maps raw seismograms to a low‐dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path‐agnostic embedding space forms a new representation for seismograms, characterized by robust, source‐specific features, which we show to be useful for performing both pairwise event association as well as template‐based source discrimination with a single template.
