Article,
An automatic, adaptive algorithm for refining phase picks in large seismic data sets
Bulletin of the Seismological Society of America, 92 (5): 1660--1674 (Jun 1, 2002)
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 regarding waveform similarity as a function
of preliminary hypocenter estimates, but also facilitates high-resolution
relocation of seismic sources. Although knowledgeable user input
is needed initially to establish run-time parameters, significant
improvement in pick consistency and waveform-based event classification
may be obtained by then allowing the programs to operate automatically
on the data. The process shows promise for enhancing catalog reliability
while at the same time reducing analyst workload, although careful
assessment of the automatic results is still important.
