1 research outputs found
Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity
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