Quantifying Similarity in Seismic Polarizations

Measuring similarity in seismic attributes can help identify tremor, low S/N signals, and converted or reflected phases, in addition to diagnosing site noise and sensor misalignment in arrays. Polarization analysis is a widely accepted method for studying the orientation and directional characteristics of seismic phases via. computed attributes, but similarity is ordinarily discussed using qualitative comparisons with reference values. Here we introduce a technique for quantitative polarization similarity that uses weighted histograms computed in short, overlapping time windows, drawing on methods adapted from the image processing and computer vision literature. Our method accounts for ambiguity in azimuth and incidence angle and variations in signal-to-noise (S/N) ratio. Using records of the Mw=8.3 Sea of Okhotsk earthquake from CNSN broadband sensors in British Columbia and Yukon Territory, Canada, and vertical borehole array data from a monitoring experiment at Hoadley gas field, central Alberta, Canada, we demonstrate that our method is robust to station spacing. Discrete wavelet analysis extends polarization similarity to the time-frequency domain in a straightforward way. Because histogram distance metrics are bounded by [0 1], clustering allows empirical time-frequency separation of seismic phase arrivals on single-station three-component records. Array processing for automatic seismic phase classification may be possible using subspace clustering of polarization similarity, but efficient algorithms are required to reduce the dimensionality.