Abstract
Large digital seismic data sets typically have approximate and inconsistent P and S wave arrival estimates. If not corrected, this situation frequently blurs the true hypocenter distribution so as to mask important fault, volcanic, or other seismogenic structure. Recent applications which address this problem for increasingly large data sets have resulted in dramatic illumination of seismogenic features in hydrocarbon and geothermal reservoirs, as well as in tectonic and volcanic earthquake regions. Existing algorithms frequently require substantial analyst interaction, which can become prohibitive once more than a few tens of events are considered. We summarize progress towards developing a fully automatic technique for removing pick inconsistencies and subsequently re-estimating hypocenters, and discuss our development of an adaptive, correlation-based algorithm which points the way towards techniques that appear suitable for the largest data sets presently existing.
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Aster, R., Rowe, C. (2000). Automatic Phase Pick Refinement and Similar Event Association in Large Seismic Datasets. In: Thurber, C.H., Rabinowitz, N. (eds) Advances in Seismic Event Location. Modern Approaches in Geophysics, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9536-0_9
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DOI: https://doi.org/10.1007/978-94-015-9536-0_9
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