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Inverse hydrologic modeling using stochastic growth algorithms
Author(s) -
Hestir Kevin,
Martel Stephen J.,
Vail Stacy,
Long Jane,
D'Onfro Pete,
Rizer William D.
Publication year - 1998
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/98wr01549
Subject(s) - posterior probability , inverse , inverse problem , monte carlo method , probability distribution , computer science , conditional probability , algorithm , stochastic process , stochastic modelling , mathematical optimization , mathematics , statistics , bayesian probability , geometry , artificial intelligence , mathematical analysis
We present a method for inverse modeling in hydrology that incorporates a physical understanding of the geological processes that form a hydrologic system. The method is based on constructing a stochastic model that is approximately representative of these geologic processes. This model provides a prior probability distribution for possible solutions to the inverse problem. The uncertainty in the inverse solution is characterized by a conditional (posterior) probability distribution. A new stochastic simulation method, called conditional coding, approximately samples from this posterior distribution and allows study of solution uncertainty through Monte Carlo techniques. We examine a fracture‐dominated flow system, but the method can potentially be applied to any system where formation processes are modeled with a stochastic simulation algorithm.