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Title: Hydrogeological parameter estimation using geophysical data: Areview of selected techniques

Abstract

Subsurface environmental, engineering, and agriculturalinvestigations often require characterization of hydraulic parameters.For example, groundwater flow modeling is often performed through anaquifer whose hydrological properties have been created using stochasticsimulation techniques; these techniques use as input both hydraulicparameter point values and spatial correlation structure information.Conventional sampling or borehole techniques for measuring theseparameters are costly, time-consuming, and invasive. Geophysical data cancompliment direct characterization data by providing multi-dimensionaland high resolution subsurface measurements in a minimally invasivemanner. Several techniques have been developed in the preceding decadefor using joint geophysical-hydrological data to characterize thesubsurface; the purpose of this study is to review three methodologiesthat we have recently developed for use with geophysical-hydrologicaldata to estimate hydrological parameters and their spatial correlationstructures. The first two methodologies presented focus on producinghigh-resolution estimates of hydrological properties using denselysampled geophysical data and limited borehole data. Although we find thathigh-resolution geophysical data are useful for obtaining theseestimates, in practice, geophysical profiles often sample only a smallportion of the aquifer under investigation, and thus, the estimatesobtained from geophysical data may not be sufficient to completelydescribe the hydraulic properties of the aquifer volume. The third andlast section focuses on using high-resolution tomographic data togetherwith limited borehole data to infer the spatial correlation structure oflog-permeability,more » which can be used within stochastic simulationtechniques to generate parameter estimates at unsampled locations. Oursynthetic case studies suggest that collection of a few tomographicprofiles and interpretation of these profiles together with limitedwellbore data can yield hydrological point values and spatial correlationstructure information that can be used to aid numerical aquifer modelconstruction, calibration, and flow simulation. As this information istypically only obtainable from extensive hydrological sampling, use ofgeophysical methods may offer a more efficient and less invasive approachthan traditional characterization campaigns.« less

Authors:
;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Director. Office of Science. Biological andEnvironmental Research
OSTI Identifier:
917329
Report Number(s):
LBNL-50346
R&D Project: 467201; BnR: KP1301020
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Journal of Contaminant Hydrology
Additional Journal Information:
Journal Volume: 45; Journal Issue: 1-2; Related Information: Journal Publication Date: September2000
Country of Publication:
United States
Language:
English
Subject:
54; Hydrogeological parameter estimation Geophysical dataSubsurface measurements

Citation Formats

Hubbard, Susan S., and Rubin, Yoram. Hydrogeological parameter estimation using geophysical data: Areview of selected techniques. United States: N. p., 1999. Web.
Hubbard, Susan S., & Rubin, Yoram. Hydrogeological parameter estimation using geophysical data: Areview of selected techniques. United States.
Hubbard, Susan S., and Rubin, Yoram. Wed . "Hydrogeological parameter estimation using geophysical data: Areview of selected techniques". United States.
@article{osti_917329,
title = {Hydrogeological parameter estimation using geophysical data: Areview of selected techniques},
author = {Hubbard, Susan S. and Rubin, Yoram},
abstractNote = {Subsurface environmental, engineering, and agriculturalinvestigations often require characterization of hydraulic parameters.For example, groundwater flow modeling is often performed through anaquifer whose hydrological properties have been created using stochasticsimulation techniques; these techniques use as input both hydraulicparameter point values and spatial correlation structure information.Conventional sampling or borehole techniques for measuring theseparameters are costly, time-consuming, and invasive. Geophysical data cancompliment direct characterization data by providing multi-dimensionaland high resolution subsurface measurements in a minimally invasivemanner. Several techniques have been developed in the preceding decadefor using joint geophysical-hydrological data to characterize thesubsurface; the purpose of this study is to review three methodologiesthat we have recently developed for use with geophysical-hydrologicaldata to estimate hydrological parameters and their spatial correlationstructures. The first two methodologies presented focus on producinghigh-resolution estimates of hydrological properties using denselysampled geophysical data and limited borehole data. Although we find thathigh-resolution geophysical data are useful for obtaining theseestimates, in practice, geophysical profiles often sample only a smallportion of the aquifer under investigation, and thus, the estimatesobtained from geophysical data may not be sufficient to completelydescribe the hydraulic properties of the aquifer volume. The third andlast section focuses on using high-resolution tomographic data togetherwith limited borehole data to infer the spatial correlation structure oflog-permeability, which can be used within stochastic simulationtechniques to generate parameter estimates at unsampled locations. Oursynthetic case studies suggest that collection of a few tomographicprofiles and interpretation of these profiles together with limitedwellbore data can yield hydrological point values and spatial correlationstructure information that can be used to aid numerical aquifer modelconstruction, calibration, and flow simulation. As this information istypically only obtainable from extensive hydrological sampling, use ofgeophysical methods may offer a more efficient and less invasive approachthan traditional characterization campaigns.},
doi = {},
journal = {Journal of Contaminant Hydrology},
number = 1-2,
volume = 45,
place = {United States},
year = {1999},
month = {8}
}