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Title: Micro-Scale Inverse Modeling.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Nuclear Weapons Development and Material Production Detection (MPD) Program Review Meeting held April 28-30, 2015 in Los Alamos, NM.
Country of Publication:
United States

Citation Formats

Reichardt, Thomas A., and Kulp, Thomas J. Micro-Scale Inverse Modeling.. United States: N. p., 2015. Web.
Reichardt, Thomas A., & Kulp, Thomas J. Micro-Scale Inverse Modeling.. United States.
Reichardt, Thomas A., and Kulp, Thomas J. 2015. "Micro-Scale Inverse Modeling.". United States. doi:.
title = {Micro-Scale Inverse Modeling.},
author = {Reichardt, Thomas A. and Kulp, Thomas J},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 4

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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  • Calibration of the LBL-USGS site-scale model of Yucca Mountain is initiated. Inverse modeling techniques are used to match the results of simplified submodels to the observed pressure, saturation, and temperature data. Hydrologic and thermal parameters are determined and compared to the values obtained from laboratory measurements and conventional field test analysis.
  • Multi-scale binary permeability field estimation from static and dynamic data is completed using Markov Chain Monte Carlo (MCMC) sampling. The binary permeability field is defined as high permeability inclusions within a lower permeability matrix. Static data are obtained as measurements of permeability with support consistent to the coarse scale discretization. Dynamic data are advective travel times along streamlines calculated through a fine-scale field and averaged for each observation point at the coarse scale. Parameters estimated at the coarse scale (30 x 20 grid) are the spatially varying proportion of the high permeability phase and the inclusion length and aspect ratiomore » of the high permeability inclusions. From the non-parametric, posterior distributions estimated for these parameters, a recently developed sub-grid algorithm is employed to create an ensemble of realizations representing the fine-scale (3000 x 2000), binary permeability field. Each fine-scale ensemble member is instantiated by convolution of an uncorrelated multiGaussian random field with a Gaussian kernel defined by the estimated inclusion length and aspect ratio. Since the multiGaussian random field is itself a realization of a stochastic process, the procedure for generating fine-scale binary permeability field realizations is also stochastic. Two different methods are hypothesized to perform posterior predictive tests. Different mechanisms for combining multi Gaussian random fields with kernels defined from the MCMC sampling are examined. Posterior predictive accuracy of the estimated parameters is assessed against a simulated ground truth for predictions at both the coarse scale (effective permeabilities) and at the fine scale (advective travel time distributions). The two techniques for conducting posterior predictive tests are compared by their ability to recover the static and dynamic data. The skill of the inference and the method for generating fine-scale binary permeability fields are evaluated through flow calculations on the resulting fields using fine-scale realizations and comparing them against results obtained with the ground truth fine-scale and coarse-scale permeability fields.« less
  • Abstract not provided.