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High-precision earthquake location using source-specific station terms and inter-event waveform similarity
  • Anthony Lomax,
  • Alexandros Savvaidis
Anthony Lomax
ALomax Scientific, ALomax Scientific

Corresponding Author:alomax@free.fr

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Alexandros Savvaidis
University of Texas at Austin, University of Texas at Austin
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Earthquake monitoring and many seismological studies depend on earthquake locations from phase arrival-times. We present an extended, arrival-time earthquake location procedure (NLL-SSST- coherence) which approaches the precision of differential-timing based, relative location methods and is applicable with few seismic stations. NLL-SSST-coherence is based on the probabilistic, global-search NonLinLoc (NLL) location algorithm which defines a probability density function (PDF) in 3D space for hypocenter location and is highly robust to outlier data. NLL-SSST-coherence location first reduces velocity model error through iteratively generated, smooth, source-specific, station travel-time corrections (SSST). Next, arrival-time error is reduced by consolidating location information across events based on inter-event waveform coherency. If the waveforms at a station for multiple events are very similar (have high coherency) up to a given frequency, then the distance separating these “multiplet” events is small relative to the seismic wavelength at that frequency. NLL-coherence relocation for a target event is a stack over 3D space of the NLL-SSST location PDF for the event and the PDF’s for other multiplet events, each weighted by its waveform coherency with the target. NLL-coherence relocation requires waveforms from only one or a few seismic stations, enabling precise relocation with sparse networks, for foreshocks and early aftershocks of significant events before installation of temporary stations, and for older data sets with few waveform data. We show the behavior and performance of NLL-SSST-coherence with synthetic and ground-truth tests, and through application and comparison to relative locations for California earthquake sequences with dense and sparse station coverage.
Jan 2022Published in Journal of Geophysical Research: Solid Earth volume 127 issue 1. 10.1029/2021JB023190