A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment
| Autor: | Jianlin Fu |
|---|---|
| Přispěvatelé: | Gómez Hernández, José Jaime, Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Independent and identically distributed random variables
Mathematical optimization INGENIERIA HIDRAULICA History matching 330515 - Ingeniería hidráulica Computer science Spatial structure Computation Model calibration Conditional simulation Blocking (statistics) symbols.namesake 250605 - Hidrogeología Stochastic simulation Sensitivity (control systems) Geostatistics 3305 15 Markov chain Mcmc 2506 05 Markov chain Monte Carlo Reservoir simulation Inverse problem Statistics::Computation 2508 00 symbols 1209 - Estadística 2508 - Hidrología 1209 00 |
| Zdroj: | Riunet RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| Popis: | Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics. First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk particle-tracking method, which enhances the com Fu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. doi:10.4995/Thesis/10251/1969. |
| Databáze: | OpenAIRE |
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