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Bayesian inversion for finite fault earthquake source models I—theory and algorithm

Minson, S. E. and Simons, M. and Beck, J. L. (2013) Bayesian inversion for finite fault earthquake source models I—theory and algorithm. Geophysical Journal International . ISSN 0956-540X. doi:10.1093/gji/ggt180.

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The estimation of finite fault earthquake source models is an inherently underdetermined problem: there is no unique solution to the inverse problem of determining the rupture history at depth as a function of time and space when our data are limited to observations at the Earth’s surface. Bayesian methods allow us to determine the set of all plausible source model parameters that are consistent with the observations, our a priori assumptions about the physics of the earthquake source and wave propagation, and models for the observation errors and the errors due to the limitations in our forward model. Because our inversion approach does not require inverting any matrices other than covariance matrices, we can restrict our ensemble of solutions to only those models that are physically defensible while avoiding the need to restrict our class of models based on considerations of numerical invertibility. We only use prior information that is consistent with the physics of the problem rather than some artefice (such as smoothing) needed to produce a unique optimal model estimate. Bayesian inference can also be used to estimate model-dependent and internally consistent effective errors due to shortcomings in the forward model or data interpretation, such as poor Green’s functions or extraneous signals recorded by our instruments. Until recently, Bayesian techniques have been of limited utility for earthquake source inversions because they are computationally intractable for problems with as many free parameters as typically used in kinematic finite fault models. Our algorithm, called cascading adaptive transitional metropolis in parallel (CATMIP), allows sampling of high-dimensional problems in a parallel computing framework. CATMIP combines the Metropolis algorithm with elements of simulated annealing and genetic algorithms to dynamically optimize the algorithm’s efficiency as it runs. The algorithm is a generic Bayesian Markov Chain Monte Carlo sampler; it works independently of the model design, a priori constraints and data under consideration, and so can be used for a wide variety of scientific problems. We compare CATMIP’s efficiency relative to several existing sampling algorithms and then present synthetic performance tests of finite fault earthquake rupture models computed using CATMIP.

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Minson, S. E.0000-0001-5869-3477
Simons, M.0000-0003-1412-6395
Additional Information:© The Authors 2013. Published by Oxford University Press on behalf of The Royal Astronomical Society. Accepted 2013 May 2. Received 2013 May 1; in original form 2012 October 15. First published online: June 19, 2013. The authors would like to thank Michael Aivazis for helpful discussions. This work is supported by the National Science Foundation through grant number EAR-0941374 and is Caltech Seismological Laboratory contribution 10086.
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Subject Keywords:Inverse theory; Probability distributions; Computational seismology
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Caltech Seismological Laboratory10086
Record Number:CaltechAUTHORS:20130801-112620316
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:39705
Deposited By: Kristin Buxton
Deposited On:01 Aug 2013 20:04
Last Modified:09 Nov 2021 23:46

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