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Title: Joint Hydrological-Geophysical Inversion for Soil StructureIdentification

Abstract

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous system, and (2) site-specific data that are sufficiently sensitive to the processes of interest. We present a stochastic approach where the high-resolution imaging capability of geophysical methods is combined with the process-specific information obtained from the inversion of hydrological data. Geostatistical concepts are employed as a flexible means to describe and characterize subsurface structures. The key features of the proposed approach are (1) the joint inversion of geophysical and hydrological raw data, avoiding the intermediate step of creating a (non-unique and potentially biased) tomogram of geophysical properties, (2) the concurrent estimation of hydrological and petrophysical parameters in addition to (3) the determination of geostatistical parameters from the joint inversion of hydrological and geophysical data; this approach is fundamentally different from inference of geostatistical parameters from an analysis of spatially distributed property data. The approach has been implemented into the iTOUGH2 inversion code and is demonstrated formore » the joint use of synthetic time-lapse ground-penetrating radar (GPR) travel times and hydrological data collected during a simulated ponded infiltration experiment at a highly heterogeneous site.« less

Authors:
;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Laboratory Directed Research andDevelopment
OSTI Identifier:
889811
Report Number(s):
LBNL-60088
R&D Project: 366165; TRN: US200619%%860
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: TOUGH Symposium 2006, LBNL, Berkeley, California,May 15-17, 2006
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; ACCURACY; FORECASTING; RADAR; SOILS; SPATIAL DISTRIBUTION; SUBSURFACE STRUCTURES; TRANSPORT; Hydrogeophysics iTOUGH2 Ground Penetrating Radar

Citation Formats

Finsterle, Stefan, and Kowalsky, Michael B. Joint Hydrological-Geophysical Inversion for Soil StructureIdentification. United States: N. p., 2006. Web.
Finsterle, Stefan, & Kowalsky, Michael B. Joint Hydrological-Geophysical Inversion for Soil StructureIdentification. United States.
Finsterle, Stefan, and Kowalsky, Michael B. Mon . "Joint Hydrological-Geophysical Inversion for Soil StructureIdentification". United States. doi:. https://www.osti.gov/servlets/purl/889811.
@article{osti_889811,
title = {Joint Hydrological-Geophysical Inversion for Soil StructureIdentification},
author = {Finsterle, Stefan and Kowalsky, Michael B.},
abstractNote = {Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous system, and (2) site-specific data that are sufficiently sensitive to the processes of interest. We present a stochastic approach where the high-resolution imaging capability of geophysical methods is combined with the process-specific information obtained from the inversion of hydrological data. Geostatistical concepts are employed as a flexible means to describe and characterize subsurface structures. The key features of the proposed approach are (1) the joint inversion of geophysical and hydrological raw data, avoiding the intermediate step of creating a (non-unique and potentially biased) tomogram of geophysical properties, (2) the concurrent estimation of hydrological and petrophysical parameters in addition to (3) the determination of geostatistical parameters from the joint inversion of hydrological and geophysical data; this approach is fundamentally different from inference of geostatistical parameters from an analysis of spatially distributed property data. The approach has been implemented into the iTOUGH2 inversion code and is demonstrated for the joint use of synthetic time-lapse ground-penetrating radar (GPR) travel times and hydrological data collected during a simulated ponded infiltration experiment at a highly heterogeneous site.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}

Conference:
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