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Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography
Author(s) -
Wang Kai,,
Liu Qinya,
Yang Yingjie,
Publication year - 2019
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2018jb017020
Subject(s) - tomography , ambient noise level , sensitivity (control systems) , noise (video) , kernel (algebra) , isotropy , rotation (mathematics) , transverse plane , acoustics , physics , mathematical analysis , mathematics , computer science , geometry , optics , artificial intelligence , structural engineering , combinatorics , image (mathematics) , sound (geography) , engineering , electronic engineering
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 V S H 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.