Abstract

Accurate determination of seismic velocity of the crust is important for understanding regional tectonics and crustal evolution of the Earth. We propose a stepwise joint linearized inversion method using surface wave dispersion, Rayleigh wave ZH ratio (i.e., ellipticity), and receiver function data to better resolve 1D crustal shear wave velocity (vS) structure. Surface wave dispersion and Rayleigh wave ZH ratio data are more sensitive to absolute variations of shear wave speed at depths, but their sensitivity kernels to shear wave speeds are different and complimentary. However, receiver function data are more sensitive to sharp velocity contrast (e.g., due to the existence of crustal interfaces) and vP/vS ratios. The stepwise inversion method takes advantages of the complementary sensitivities of each dataset to better constrain the vS model in the crust. We firstly invert surface wave dispersion and ZH ratio data to obtain a 1D smooth absolute vS model and then incorporate receiver function data in the joint inversion to obtain a finer vS model with better constraints on interface structures. Through synthetic tests, Monte Carlo error analyses, and application to real data, we demonstrate that the proposed joint inversion method can resolve robust crustal vS structures and with little initial model dependency.

Highlights

  • The crustal structure of the Earth is fundamental to our understanding of the formation mechanisms and evolution processes of the crust as well as dynamics of the Earth’s deep interior

  • We propose a stepwise linearized inversion algorithm to jointly invert surface wave dispersion, Rayleigh wave ZH ratio, and receiver function data to better resolve 1D crustal vS structure

  • Receiver function (RF) and surface wave data have initial model dependency with linear inversion strategy (Ammon et al 1990; Yano et al 2009; Lebedev et al 2013), so we performed the stepwise joint inversion using a variety of different starting models to investigate its initial model dependency

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Summary

Introduction

The crustal structure of the Earth is fundamental to our understanding of the formation mechanisms and evolution processes of the crust as well as dynamics of the Earth’s deep interior. RF is sensitive to the discontinuities in the crust and upper mantle beneath seismic stations, but the inversion of RF for crustal vS structures is intrinsically nonunique and highly depends on the initial model (e.g., Langston 1979; Owens et al 1984; Ammon et al 1990; Ammon 1991). It cannot well constrain absolute velocity structures alone.

Methodology
Inversion with noise-free data
Effects of influence coefficients in Stage 2
Initial model dependence
Monte Carlo error analyses with random data noise
Application to real data
Conclusion
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