Development and Tuning of a 3D Stochastic Inversion Methodology to the European Arctic
The development of three-dimensional (3D) seismic models for the crust and upper mantle has traditionally focused on finding one model that provides the best fit to the data, while observing some regularization constraints. Such deterministic models, however, ignore a fundamental property of many in...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://www.dtic.mil/docs/citations/ADA569446 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA569446 |
| Summary: | The development of three-dimensional (3D) seismic models for the crust and upper mantle has traditionally focused on finding one model that provides the best fit to the data, while observing some regularization constraints. Such deterministic models, however, ignore a fundamental property of many inverse problems in geophysics: nonuniqueness. It is likely that if a model can be found to satisfy given datasets, an infinite number of alternative models will exist that satisfy the datasets equally well. Our solution to the inverse problem of developing a seismic model for the Barents Sea, given various datasets, is therefore a probabilistic model, a posterior distribution of models that satisfy the data to the same degree. We use a Markov Chain Monte Carlo algorithm to sample the unknown posterior distribution, which describes the ensemble of models that are in agreement with prior information and the datasets. Published in Proceedings of the 2010 Monitoring Research Review - Ground-Based Nuclear Explosion Monitoring Technologies, 21-23 September 2010, Orlando, FL. Volume I. Sponsored by the Air Force Research Laboratory (AFRL) and the National Nuclear Security Administration (NNSA). U.S. Government or Federal Rights License |
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