Manuscript is a non-peer reviewed preprint to be submitted to Geophysical Journal International Redshift of Earthquakes via Focused Blind Deconvolution of Teleseisms
Online Publication Date2020-09-02
Print Publication Date2020-10-15
Permanent link to this recordhttp://hdl.handle.net/10754/665056
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AbstractWe 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 high frequency content occurs at the receiver in the direction opposite to 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.
CitationBharadwaj, P., Meng, C., Fournier, A., Demanet, L., & Fehler, M. (2020). Manuscript is a non-peer reviewed preprint to be submitted to Geophysical Journal International Redshift of Earthquakes via Focused Blind Deconvolution of Teleseisms. Geophysical Journal International. doi:10.1093/gji/ggaa419
SponsorsWe thank the editor (Prof. Huajian Yao) and reviewers (Jiuxun Yin and anonymous) for providing their valuable feedback. We thank our colleagues Nafi Toks¨oz, Matt Li, Aurelien Mordret, Tom Herring, Hongjian Fang and Michael Floyd for providing insight and expertise that greatly assisted this research. We thank Martin Mai from King Abdullah University of Science and Technology for his informative commentary of a draft version. The material is based upon work assisted by a grant from Equinor. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Equinor. LD is also supported by AFOSR grant FA9550-17-1-0316.
PublisherOxford University Press (OUP)