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• W2327097191 abstract "A new method to obtain the statistics of a geostatistical model is introduced. The method elicits the statistical information from a geological expert directly, by iteratively updating a population of vectors of statistics, based on the expert's subjective opinion of the corresponding geological simulations. Thus, it does not require the expert to have knowledge of the mathematical and statistical details of the model. The process uses a genetic algorithm to generate new vectors. We demonstrate the methodology for a particular geostatistical model used to model rock pore-space, which simulates the spatial distribution of matrix and pores over a 2-D grid, using multipoint statistics specified by conditional probabilities. Experts were asked to use the algorithm to estimate the statistics of a given target pore-space image with known statistics; thus, their numerical rates of convergence could be calculated. Convergence was measured for all experts, showing that the algorithm can be used to find appropriate probabilities given the expert's subjective input. However, considerable and apparently irreducible residual misfit was found between the true statistics and the estimates of statistics obtained by the experts, with the root-mean-square error on the conditional probabilities typically >0.1. This is interpreted as the limit of the experts’ abilities to distinguish between realizations of different spatial statistics using the algorithm. More accurate discrimination is therefore likely to require complementary elicitation techniques or sources of information independent of expert opinion." @default.
• W2327097191 created "2016-06-24" @default.
• W2327097191 creator A5001742126 @default.
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• W2327097191 date "2014-05-20" @default.
• W2327097191 modified "2023-09-26" @default.
• W2327097191 title "Eliciting spatial statistics from geological experts using genetic algorithms" @default.
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• W2327097191 doi "https://doi.org/10.1093/gji/ggu132" @default.
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