Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity
This addresses a key limitation in seismic signal processing for seismologists, enabling more efficient and accurate event analysis without path-specific templates.
The paper tackled the problem of seismogram similarity being dominated by path effects, which traditionally required separate templates for each path, by proposing a path-invariant measure using a deep convolutional neural network trained on 207,291 seismograms, achieving robust source-specific features for event association and discrimination with a single template.
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 8,452 unique events is constructed, and then employed via the batch-hard triplet loss function, to train a deep convolutional neural network which 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.