HR: 17:15h
AN: S54B-06    [Abstracts]
TI: Blind source deconvolution for deep Earth seismology
AU: * Stefan, W
EM: wolfgang.stefan@asu.edu
AF: Arizona State University, Department of Mathematics, P.O. Box 871804, Tempe, AZ 85287-1804, United States
AU: Renaut, R
EM: renaut@math.asu.edu
AF: Arizona State University, Department of Mathematics, P.O. Box 871804, Tempe, AZ 85287-1804, United States
AU: Garnero, E J
EM: garnero@asu.edu
AF: Arizona State University, School of Earth & Space Exploration, PO Box 871404, Tempe, AZ 85287-1404, United States
AU: Lay, T
EM: tlay@es.ucsc.edu
AF: University of California, Santa Cruz, Earth & Planetary Sciences, 1156 High Street, Santa Cruz, CA 95064, United States
AB: We present an approach to automatically estimate an empirical source characterization of deep earthquakes recorded teleseismically and subsequently remove the source from the recordings by applying regularized deconvolution. A principle goal in this work is to effectively deblur the seismograms, resulting in more impulsive and narrower pulses, permitting better constraints in high resolution waveform analyses. Our method consists of two stages: (1) we first estimate the empirical source by automatically registering traces to their 1st principal component with a weighting scheme based on their deviation from this shape, we then use this shape as an estimation of the earthquake source. (2) We compare different deconvolution techniques to remove the source characteristic from the trace. In particular Total Variation (TV) regularized deconvolution is used which utilizes the fact that most natural signals have an underlying spareness in an appropriate basis, in this case, impulsive onsets of seismic arrivals. We show several examples of deep focus Fiji-Tonga region earthquakes for the phases S and ScS, comparing source responses for the separate phases. TV deconvolution is compared to the water level deconvolution, Tikenov deconvolution, and L1 norm deconvolution, for both data and synthetics. This approach significantly improves our ability to study subtle waveform features that are commonly masked by either noise or the earthquake source. Eliminating source complexities improves our ability to resolve deep mantle triplications, waveform complexities associated with possible double crossings of the post-perovskite phase transition, as well as increasing stability in waveform analyses used for deep mantle anisotropy measurements.
DE: 3260 Inverse theory
DE: 3270 Time series analysis (1872, 4277, 4475)
DE: 7203 Body waves
DE: 7290 Computational seismology
SC: Seismology [S]
MN: 2007 Fall Meeting