Redshift of Earthquakes via Focused Blind Deconvolution of Teleseisms

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 3 of this Preprint.


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Pawan Bharadwaj, Chunfang Meng, Aimé Fournier, Laurent Demanet, Mike Fehler


We present a robust factorization of the teleseismic waveforms resulting from an earthquake source into signals that originate from the source and signals that characterize the path effects. The extracted source signals represent the earthquake spectrum and its variation with azimuth. Unlike most prior work on source extraction, our method is data-driven, and it does not depend on any path-related assumptions e.g., the empirical Green’s function. Instead, our formulation involves focused blind deconvolution (FBD), which associates the source characteristics with the similarity among a multitude of recorded signals. We also introduce a new spectral attribute, to be called redshift, which is based on the Fraunhofer approximation. Redshift describes source-spectrum variation, where a decrease in frequency occurs at the receiver in the opposite direction of unilateral rupture propagation. Using the redshift, we identified unilateral ruptures during two recent strike-slip earthquakes. The FBD analysis of an earthquake, which originated in the eastern California shear zone, is consistent with observations from local seismological or geodetic instrumentation.



Applied Mathematics, Earth Sciences, Electrical and Computer Engineering, Engineering, Geophysics and Seismology, Physical Sciences and Mathematics, Signal Processing


Surface waves, Earthquake source, Andaman, blind deconvolution, Doppler effect, fault inversion, focusing, Fraunhofer, redshift, Ridgecrest, rupture velocity


Published: 2019-10-13 05:52

Last Updated: 2020-03-30 14:50

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GNU Lesser General Public License (LGPL) 2.1

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