Receiver function inversion by trans-dimensional Monte Carlo sampling

Details Receiver function inversion by trans-dimensional Monte Carlo sampling Receiver function inversion by trans-dimensional Monte Carlo sampling

May 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010geoji.181..858a&link_type=abstract

Geophysical Journal International, Volume 181, Issue 2, pp. 858-872.

Statistics

Computation

5

Time Series Analysis, Inverse Theory, Computational Seismology

Scientific paper

A key question in the analysis of an inverse problem is the quantification of the non-uniqueness of the solution. Non-uniqueness arises when properties of an earth model can be varied without significantly worsening the fit to observed data. In most geophysical inverse problems, subsurface properties are parameterized using a fixed number of unknowns, and non-uniqueness has been tackled with a Bayesian approach by determining a posterior probability distribution in the parameter space that combines `a priori' information with information contained in the observed data. However, less consideration has been given to the question whether the data themselves can constrain the model complexity, that is the number of unknowns needed to fit the observations. Answering this question requires solving a trans-dimensional inverse problem, where the number of unknowns is an unknown itself. Recently, the Bayesian approach to parameter estimation has been extended to quantify the posterior probability of the model complexity (the number of model parameters) with a quantity called `evidence'. The evidence can be hard to estimate in a non-linear problem; a practical solution is to use a Monte Carlo sampling algorithm that samples models with different number of unknowns in proportion to their posterior probability.
This study presents a method to solve in trans-dimensional fashion the non-linear inverse problem of inferring 1-D subsurface elastic properties from teleseismic receiver function data. The Earth parameterization consists of a variable number of horizontal layers, where little is assumed a priori about the elastic properties, the number of layers, and and their thicknesses. We developed a reversible jump Markov Chain Monte Carlo algorithm that draws samples from the posterior distribution of Earth models. The solution of the inverse problem is a posterior probability distribution of the number of layers, their thicknesses and the elastic properties as a function of depth. These posterior distributions quantify completely the non-uniqueness of the solution. We illustrate the algorithm by inverting synthetic and field measurements, and the results show that the data constrain the model complexity. In the synthetic example, the main features of the subsurface properties are recovered in the posterior probability distribution. The inversion results for actual measurements show a crustal structure that agrees with previous studies in both crustal thickness and presence of intracrustal low-velocity layers.

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