Three-dimensional sensitivity kernels for multicomponent empirical Green's functions from ambient noise

methodology and application to adjoint tomography

Kai Wang, Qinya Liu, Yingjie Yang*

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Citations (Scopus)
13 Downloads (Pure)

Abstract

Adjoint tomography has recently been applied to ambient noise data as a new and promising tomographic method that utilizes simulation-based 3-D sensitivity kernels rather than ray theory used in traditional ambient noise tomography. However, to date, most studies of ambient noise adjoint tomography only use vertical-component Rayleigh waves. In this study, we develop a theoretical framework for calculating sensitivity kernels for multicomponent empirical Green's functions extracted from ambient noise data. Under the framework of the adjoint method, we demonstrate that a horizontal component (transverse-transverse or radial-radial) kernel can be constructed from the interaction of wave fields generated by point-force sources acting in the north and east directions based on rotation relationships. Our method is benchmarked for a 3-D heterogeneous isotropic model by comparing rotated seismograms, individual, and event traveltime misfit kernels with corresponding references computed by numerical simulations with sources directly placed in the radial or transverse directions. Based on our new method, we perform the first Love-wave ambient noise adjoint tomography in southern California and construct an improved VSH model. Our method for computing sensitivity kernels of multicomponent empirical Green's functions provides the basis for multicomponent ambient noise adjoint tomography in imaging radially anisotropic shear-wave velocity structures.

Original languageEnglish
Pages (from-to)5794-5810
Number of pages17
JournalJournal of Geophysical Research: Solid Earth
Volume124
Issue number6
DOIs
Publication statusPublished - Jun 2019

Bibliographical note

Copyright 2019 American Geophysical Union.

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