### Abstract

Original language | English |
---|---|

Article number | B02301 |

Number of pages | 24 |

Journal | Journal of Geophysical Research |

Volume | 117 |

Issue number | B2 |

Early online date | 3 Feb 2012 |

DOIs | |

Publication status | Published - Feb 2012 |

### Fingerprint

### Keywords

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

### Cite this

*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.

Research output: Contribution to journal › Article

*Journal of Geophysical Research*, vol. 117, no. B2, B02301. https://doi.org/10.1029/2011JB008560

}

TY - JOUR

T1 - Transdimensional inversion of receiver functions and surface wave dispersion

AU - Bodin, T.

AU - Sambridge, M.

AU - Tkalcic, H.

AU - Arroucau, P.

AU - Gallagher, K.

AU - Rawlinson, N.

N1 - Acknowledgments. This research was supported under Australian Research Council Discovery projects funding scheme (project DP110102098).This project was also supported by French-Australian Science and Technology travel grant (FR090051) under the International Science Linkages pro-gram from the Department of Innovation, Industry, Science and Research.Calculations were performed on the Terrawulf II cluster, a computationalfacility supported through AuScope. Auscope Ltd is funded under the National Collaborative Research Infrastructure Strategy (NCRIS), an Australian Commonwealth Government Programme. Computer software implementing the algorithms described in this paper are available from the authors

PY - 2012/2

Y1 - 2012/2

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Monte Carlo methods

KW - inverse theory

KW - receiver function

KW - surface waves

KW - time series analysis

U2 - 10.1029/2011JB008560

DO - 10.1029/2011JB008560

M3 - Article

VL - 117

JO - Journal of Geophysical Research

JF - Journal of Geophysical Research

SN - 0148-0227

IS - B2

M1 - B02301

ER -