Joint inversion of surface wave dispersion and receiver functions: a Bayesian Monte-Carlo approach
A non-linear Bayesian Monte-Carlo method is presented to estimate a Vsv model beneath stations by jointly interpreting Rayleigh wave dispersion and receiver functions and associated uncertainties. The method is designed for automated application to large arrays of broad-band seismometers. As a testbed for the method, 185 stations from the USArray Transportable Array are used in the IntermountainWest, a region that is geologically diverse and structurally complex. Ambient noise and earthquake tomography are updated by applying eikonal and Helmholtz tomography, respectively, to construct Rayleighwave dispersion maps from 8 to 80 s across the study region with attendant uncertainty estimates.Amethod referred to as 'harmonic stripping method' is described and applied as a basis for quality control and to generate backazimuth independent receiver functions for a horizontally layered, isotropic effective medium with uncertainty estimates for each station. A smooth parametrization between (as well as above and below) discontinuities at the base of the sediments and crust suffices to fit most features of both data types jointly across most of the study region. The effect of introducing receiver functions to surface wave dispersion data is quantified through improvements in the posterior marginal distribution of model variables. Assimilation of receiver functions quantitatively improves the accuracy of estimates of Moho depth, improves the determination of the Vsv contrast across Moho, and improves uppermost mantle structure because of the ability to relax a priori constraints. The method presented here is robust and can be applied systematically to construct a 3-D model of the crust and uppermost mantle across the large networks of seismometers that are developing globally, but also provides a framework for further refinements in the method.
Additional Information© 2012 The Authors. Published by Oxford University Press on behalf of The Royal Astronomical Society. Accepted 2012 October 29. Received 2012 October 16; in original form 2012 May 01. First published online: November 30, 2012. The authors gratefully acknowledge insightful reviews from Thomas Bodin, Malcolm Sambridge, an anonymous reviewer and the Associate Editor, Gabi Laske, that helped to improve this paper. They are also grateful to Craig Jones for insights into receiver function analyses and to Anne Sheehan for discussions concerning the history of joint inversions with receiver functions and surface wave dispersion. The facilities of the IRIS Data Management System, and specifically the IRIS Data Management Center, were used to access the waveform and metadata required in this study. The IRIS DMS is funded through the National Science Foundation and specifically the GEO Directorate through the Instrumentation and Facilities Program of the National Science Foundation under Cooperative Agreement EAR-0552316. This research was supported by NSF grants EAR-0711526, EAR-0844097, EAR-0750035 and EAR-1053291 at the University of Colorado at Boulder. F.-C. Lin is supported by the Director's Post-Doctoral Fellowship of the Seismological Laboratory at the California Institute of Technology.
Published - Geophys._J._Int.-2013-Shen-807-36.pdf