Forward modeling of ground‐penetrating radar data using digitized outcrop images and multiple scenarios of water saturation

Kowalsky, Michael B.; Dietrich, Peter; Teutsch, Georg; Rubin, Yoram

doi:10.1029/2001wr900015

Cited by 46 publications

(29 citation statements)

References 24 publications

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“…Separation of transmitter and receiver was held constant at 1.4 m. Asprion and Aigner (1999) provide a complete description of the field measurement procedure. In a related study, Kowalsky et al (2001) selected one section of the Herten case study to construct models with various water saturation distributions for GPR forward modeling.…”

Section: Gpr and Radar Faciesmentioning

confidence: 99%

“…Meanwhile, several recent studies have used part of the data set for demonstration of geophysical forward (Kowalsky et al, 2001) or inverse modeling (Hu et al, 2009), for hydraulic or transport modeling (Maier et al, 2005;Maji et al, 2006;Werth et al, 2006), and for sedimentological analysis (Heinz et al, 2003). The developed dataset is provided as downloadable supplement and may be used in realistic 3D hydrogeological modeling studies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-dimensional high resolution fluvio-glacial aquifer analog: Part 1: Field study

Bayer

Huggenberger

Renard

et al. 2011

Journal of Hydrology

100

106

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s u m m a r yDescribing the complex structures that exist in many sedimentary aquifers is crucial for reliable groundwater flow and transport simulation. However, hardly any aquifer can be inspected in such detail that all decimeter to meter heterogeneity is resolved. Aquifer analogs serve as surrogates to construct models of equivalent heterogeneity, and thus imitate those features relevant for flow or transport processes. Gravel pits found in excavation show excellent sections of the sedimentary sequence and thus offer direct insight into the structural and textural composition of the subsoil. This paper describes an approach to also inspect the third dimension: by mapping during the ongoing excavation it is possible to obtain a three-dimensional representation of the subsurface within a short period of time. A detailed description of a case study is presented and the findings from sedimentological, hydrogeological and geophysical analyses are compared. The gravel pit is located near the town of Herten in southwest Germany, where relatively young unconsolidated fluvio-glacial and fluvial sediments in the Rhine basin are mined. The excavated gravel body is built up by architectural elements typical for braided river deposits. The study generated a high-resolution data set of lithofacies, hydrofacies and ground penetrating radar (GPR) profiles. It represents the basis for a full three-dimensional geostatistical reconstruction presented in the second part

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Section: Gpr and Radar Faciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-dimensional high resolution fluvio-glacial aquifer analog: Part 1: Field study

Bayer

Huggenberger

Renard

et al. 2011

Journal of Hydrology

100

106

View full text Add to dashboard Cite

show abstract

“…In the last couple of decades geophysical techniques such as those described in Table 3 have become more common for hydrogeological applications, for further reading see e.g. Regli, et al, 2002;Hubbard & Rubin, 2000, Kowalsky et al, 2001Rea and Knight, 1998;Rubin & Hubbard, 2005;Veerecken et al, 2006.…”

Section: Non-uniform Infiltrationmentioning

confidence: 99%

Research for Investigating and Managing Soil Contamination Caused by Winter Maintenance in Cold Regions

French¹,

Zee²

2011

Soil Contamination

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“…GPR forward models generally fall into two categories: (1) full-waveform methods (for example, (Casper & Kung, 1996;Michael B. Kowalsky, Dietrich, Teutsch, & Rubin, 2001;Vasco, Peterson, & Lee, 1997)) and (2) ray-based methods (i.e., (Cai & Mcmechan, 1995;Peterson, 2001;Zhang, Rector, & Hoversten, 2005)). Full waveform methods compute solutions to Maxwell's equations, and offer thorough information on EM energy propagation.…”

Section: Gpr Backgroundmentioning

confidence: 99%

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

Hou¹,

Terry²,

Hubbard³

2013

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SummaryIn this study, we evaluate the possibility of monitoring soil moisture variation using tomographic ground penetrating radar travel time data through Bayesian inversion, which is integrated with entropy memory function and pilot point concepts, as well as efficient sampling approaches.It is critical to accurately estimate soil moisture content and variations in vadose zone studies.Many studies have illustrated the promise and value of GPR tomographic data for estimating soil moisture and associated changes, however, challenges still exist in the inversion of GPR tomographic data in a manner that quantifies input and predictive uncertainty, incorporates multiple data types, handles non-uniqueness and nonlinearity, and honors time-lapse tomograms collected in a series. To address these challenges, we develop a minimum relative entropy (MRE)-Bayesian based inverse modeling framework that non-subjectively defines prior probabilities, incorporates information from multiple sources, and quantifies uncertainty. The framework enables us to estimate dielectric permittivity at pilot point locations distributed within the tomogram, as well as the spatial correlation range. In the inversion framework, MRE is first used to derive prior probability distribution functions (pdfs) of dielectric permittivity based on prior information obtained from a straight-ray GPR inversion. The probability distributions are then sampled using a Quasi-Monte Carlo (QMC) approach, and the 2 sample sets provide inputs to a sequential Gaussian simulation (SGSim) algorithm that constructs a highly resolved permittivity/velocity field for evaluation with a curved-ray GPR forward model. The likelihood functions are computed as a function of misfits, and posterior pdfs are constructed using a Gaussian kernel. Inversion of subsequent time-lapse datasets combines the Bayesian estimates from the previous inversion (as a memory function) with new data. The memory function and pilot point design takes advantage of the spatial-temporal correlation of the state variables. We first apply the inversion framework to a static synthetic example and then to a time-lapse GPR tomographic dataset collected during a dynamic experiment conducted at the Hanford Site in Richland, WA. We demonstrate that the MRE-Bayesian inversion enables us to merge various data types, quantify uncertainty, evaluate nonlinear models, and produce more detailed and better resolved estimates than straight-ray based inversion; therefore, it has the potential to improve estimates of inter-wellbore dielectric permittivity and soil moisture content and to monitor their temporal dynamics more accurately.

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Forward modeling of ground‐penetrating radar data using digitized outcrop images and multiple scenarios of water saturation

Cited by 46 publications

References 24 publications

Three-dimensional high resolution fluvio-glacial aquifer analog: Part 1: Field study

Three-dimensional high resolution fluvio-glacial aquifer analog: Part 1: Field study

Research for Investigating and Managing Soil Contamination Caused by Winter Maintenance in Cold Regions

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

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