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• W80770525 abstract "Multiple-point geostatistical simulation aims at generating realizations that reproduce pattern statistics inferred from some training source, usually a training image. The most widely used algorithm is based on solving a single normal equation at each location using the conditional probabilities inferred during the training process. Simulated annealing offers an alternative implementation that, in addition, permits to incorporate additional statistics to be matched and imposing constraints based, for example, on secondary information. This paper focuses on an innovative implementation of simulated annealing to simulate categorical variables, reproducing multiple-point statistics. It is based on a well known paradigm in computer science, namely, speculative computing. In simulated annealing, categories are initially randomly distributed. Nodes are visited iteratively and a perturbation is proposed to approach the distribution of the categories to some target statistics. A decision is made to accept or conditionally reject the change, depending on an objective function that must approach zero to match the target statistics. Rejection will occur with a probability that changes during the simulation process, as defined in the annealing schedule. Speculative computing consists of using multiple processes in parallel to pre-calculate the next step in the simulation in both situations: accepting or rejecting the change. While the decision is made in the first process, a second level of two processes is used to calculate the two possible cases and subsequent levels can also be initiated. Once the decision is made, processes that do not conform to this decision are dropped and speculations about other possible perturbations at the current simulation stage are initiated. This implementation of simulated annealing can speed up the process significantly, hence making this algorithm a reasonable alternative to current methods. An example using a geologic data set is provided to demonstrate the improvements achieved and the potential this method has for larger models. Some future work is also proposed." @default.
• W80770525 created "2016-06-24" @default.
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• W80770525 date "2010-01-01" @default.
• W80770525 modified "2023-09-25" @default.
• W80770525 title "Multiple Point Geostatistical Simulation with Simulated Annealing: Implementation Using Speculative Parallel Computing" @default.
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• W80770525 doi "https://doi.org/10.1007/978-90-481-2322-3_33" @default.
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