Development and Tuning of a 3D Stochastic Inversion Methodology to the European Arctic
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2010
- Accession Number
- ADA569446
Entities
People
- Hilmar Bungum
- Jan I. Faleide
- Juerg Hauser
- Kathleen M. Dyer
- Michael E. Pasyanos
- Stephen A. Clark