Characterizing Lithofacies from Geophysical Data Using the Bayesian Model coupled with a Fuzzy Neural Network
نویسندگان
چکیده
A Bayesian model coupled with a fuzzy neural network (BFNN) is developed to alleviate the difficulty of using geophysical data in lithology estimation when cross correlation between lithology and geophysical attributes is nonlinear. The prior estimate is inferred from borehole lithology measurements using indicator kriging based on spatial correlation, and the posterior estimate is obtained from updating of the prior using the geophysical data. The novelty of the study lies in the use of a fuzzy neural network for the inference of the likelihood function. This allows incorporating spatial correlation as well as a nonlinear cross correlation into lithology estimation. The effectiveness of the BFNN is demonstrated using synthetic data generated from measurements at the Lawrence Livemore National Laboratory (LLNL) site.
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