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Detailed Reference Information |
Hestir, K., Martel, S.J., Vail, S., Long, J., D’Onfro, P. and Rizer, W.D. (1998). Inverse hydrologic modeling using stochastic growth algorithms. Water Resources Research 34: doi: 10.1029/98WR01549. issn: 0043-1397. |
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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. ¿ 1998 American Geophysical Union |
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Abstract |
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Keywords
Hydrology, Stochastic processes, Physical Properties of Rocks, Fracture and flow, Structural Geology, Fractures and faults, Mathematical Geophysics, Inverse theory |
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Publisher
American Geophysical Union 2000 Florida Avenue N.W. Washington, D.C. 20009-1277 USA 1-202-462-6900 1-202-328-0566 service@agu.org |
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