Transdimensional inversion of receiver functions and surface wave dispersion

T. Bodin, M. Sambridge, H. Tkalcic, P. Arroucau, K. Gallagher, N. Rawlinson

Research output: Contribution to journalArticle

165 Citations (Scopus)

Abstract

We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.
Original languageEnglish
Article numberB02301
Number of pages24
JournalJournal of Geophysical Research
Volume117
Issue numberB2
Early online date3 Feb 2012
DOIs
Publication statusPublished - Feb 2012

Fingerprint

wave dispersion
Surface waves
surface wave
surface waves
receivers
wave functions
inversions
Shear waves
formulations
Covariance matrix
Markov chains
Markov processes
shear stress
S waves
inversion
waveforms
uncertainty
stations
data inversion
ambient noise

Keywords

  • Bayesian inference
  • Monte Carlo methods
  • inverse theory
  • receiver function
  • surface waves
  • time series analysis

Cite this

Bodin, T., Sambridge, M., Tkalcic, H., Arroucau, P., Gallagher, K., & Rawlinson, N. (2012). Transdimensional inversion of receiver functions and surface wave dispersion. Journal of Geophysical Research, 117(B2), [B02301]. https://doi.org/10.1029/2011JB008560

Transdimensional inversion of receiver functions and surface wave dispersion. / Bodin, T.; Sambridge, M.; Tkalcic, H.; Arroucau, P.; Gallagher, K.; Rawlinson, N.

In: Journal of Geophysical Research, Vol. 117, No. B2, B02301, 02.2012.

Research output: Contribution to journalArticle

Bodin, T, Sambridge, M, Tkalcic, H, Arroucau, P, Gallagher, K & Rawlinson, N 2012, 'Transdimensional inversion of receiver functions and surface wave dispersion', Journal of Geophysical Research, vol. 117, no. B2, B02301. https://doi.org/10.1029/2011JB008560
Bodin T, Sambridge M, Tkalcic H, Arroucau P, Gallagher K, Rawlinson N. Transdimensional inversion of receiver functions and surface wave dispersion. Journal of Geophysical Research. 2012 Feb;117(B2). B02301. https://doi.org/10.1029/2011JB008560
Bodin, T. ; Sambridge, M. ; Tkalcic, H. ; Arroucau, P. ; Gallagher, K. ; Rawlinson, N. / Transdimensional inversion of receiver functions and surface wave dispersion. In: Journal of Geophysical Research. 2012 ; Vol. 117, No. B2.
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