%ANewmark, R L %AAines, R D %ANitao, J J %AHanley, W G %ACarle,
S %ARamirez, A L %ASengupta, S %AHarris, D B %D2001%K58
GEOSCIENCES; ACCURACY; DISTRIBUTION; ELECTRIC CONDUCTIVITY;
ENGINES; EVALUATION; GROUND WATER; LITHOLOGY; SIMULATORS;
TRANSPORT %MOSTI ID: 15004639 %PMedium: ED; Size: 17 pages
%TStochastic Engine: Direct Incorporation of Measurements Into
Predictive Simulations %Uhttp://www.osti.gov/scitech//servlets/p
url/15004639-rW5Io8/native/ %XWe are creating a new method of
combining disparate types of geologic observations and process
simulations. Using Bayesian inferencing and an efficient search
algorithm, we obtain a consolidated body of knowledge in the
form of multiple configurations and parameter values of the
system that are consistent with our existing data and process
models. In so doing, we effectively estimate the distributions
of both individual parameters and system-wide states, and their
likelihood of occurrence. This is in contrast with conventional
inversion methods, which produce a si