An automatic, adaptive algorithm for refining phase picks in large seismic data sets

Bulletin of the Seismological Society of America (Impact Factor: 1.94). 06/2002; 92:1660-1674. DOI: 10.1785/0120010224

ABSTRACT We have developed an adaptive, automatic, correlation- and clustering-based method for greatly reducing the degree of picking inconsistency in large, digital seismic catalogs and for quantifying similarity within, and discriminating among, clusters of disparate waveform families. Innovations in the technique include (1) the use of eigenspectral methods for cross-spectral phase estimation and for providing subsample pick lag error estimates in units of time, as opposed to dimensionless relative scaling of uncertainties; (2) adaptive, cross-coherency-based filtering; and (3) a hierarchical waveform stack correlation method for adjusting mean intercluster pick times without compromising tight intracluster relative pick estimates. To solve the systems of cross-correlation lags we apply an iterative, optimized conjugate gradient technique that minimizes an L1-norm misfit. Our repicking technique not only provides robust similarity classification-event discrimination without making a priori assumptions regar


Full-text (2 Sources)

Available from
Jun 2, 2014