Final report for "Development of generalized mapping tools to improve implementation of data driven computer simulations" (LDRD 04-ERD-083)

Pasyanos, M ; Ramirez, A ; Franz, G

Lawrence Livermore National Laboratory

关键词: Nuclear Explosions;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    National Security;    Path Integrals;    Computers;   

DOI  :  10.2172/917508
RP-ID  :  UCRL-TR-209573
RP-ID  :  W-7405-ENG-48
RP-ID  :  917508

美国|英语

来源: UNT Digital Library

【 摘 要 】

Probabilistic inverse techniques, like the Markov Chain Monte Carlo (MCMC) algorithm, have had recent success in combining disparate data types into a consistent model. The Stochastic Engine (SE) initiative was a technique that developed this method and applied it to a number of earth science and national security applications. For instance, while the method was originally developed to solve ground flow problems (Aines et al.), it has also been applied to atmospheric modeling and engineering problems. The investigators of this proposal have applied the SE to regional-scale lithospheric earth models, which have applications to hazard analysis and nuclear explosion monitoring. While this broad applicability is appealing, tailoring the method for each application is inefficient and time-consuming. Stochastic methods invert data by probabilistically sampling the model space and comparing observations predicted by the proposed model to observed data and preferentially accepting models that produce a good fit, generating a posterior distribution. In other words, the method ''inverts'' for a model or, more precisely, a distribution of models, by a series of forward calculations. While powerful, the technique is often challenging to implement, as the mapping from model space to data needs to be ''customized'' for each data type. For example, all proposed models might need to be transformed through sensitivity kernels from 3-D models to 2-D models in one step in order to compute path integrals, and transformed in a completely different manner in the next step. We seek technical enhancements that widen the applicability of the Stochastic Engine by generalizing some aspects of the method (i.e. model-to-data transformation types, configuration, model representation). Initially, we wish to generalize the transformations that are necessary to match the observations to proposed models. These transformations are sufficiently general not to pertain to any single application. This is a new and innovative approach to the problem, providing a framework to increase the efficiency of its implementation. The overall goal is to reduce response time and make the approach as ''plug-and-play'' as possible, and will result in the rapid accumulation of new data types for a host of both earth science and non-earth science problems.