Parameter adjustment of indicator variograms for groundwater flow modeling using genetic algorithms.
Conference
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OSTI ID:915161
- Florida State University, Tallahassee, FL
Current algorithms for the inverse calibration of hydraulic conductivity (K) fields to observed head data update the K values to achieve calibration but consider the parameters defining the spatial correlation of the K values to be fixed. Here we examine the ability of a genetic algorithm (GA) to update indicator variogram parameters defining the spatial correlation of the K field subject to minimizing differences between modeled and observed head values and also to minimizing the advective travel time across the model. The technique is presented on a test problem consisting of 83 K values randomly selected from 8649 gas-permeameter measurements made on a block of heterogeneous sandstone. Indicator variograms at the 10th, 40th, 60th and 90th percentiles of the cumulative log10 K distribution are used to describe the spatial variability of the log10 hydraulic conductivity data. For each threshold percentile, the variogram models are parameterized by the nugget, sill, anisotropic range values and the direction of principal correlation. The 83 conditioning data and the variogram models are used as input to a geostatistical indicator simulation algorithm.
- Research Organization:
- Sandia National Laboratories
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 915161
- Report Number(s):
- SAND2003-0487C
- Country of Publication:
- United States
- Language:
- English
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