The need to adapt the exploration model from the oil patch to contaminated-site characterization: A case from Hill AFB, Utah, USA

Bradford, John H.; Babcock, Esther

doi:10.1190/tle32070750.1

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whether a training data set is “truly” representative of a specific study area is an unanswered question because the “reality” would be always hidden. In this context, a comprehensive data acquisition approach should be developed and adopted [e.g., Bradford and Babcock , ; Paradis et al ., ] to ensure a more representative training data set. Such approaches are needed to ensure the coverage of the entire range of hydraulic and geophysical responses present over a study area. Second, the acquisition of K data for aquifer characterization based on H‐G relations leads to new ways to target hydraulic tests, which are carried out in the perspective of providing K values over the observed range of geophysical responses in a given study area.…”

Section: Discussionmentioning

confidence: 99%

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

et al. 2015

View full text Add to dashboard Cite

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km 2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

et al. 2015

View full text Add to dashboard Cite

show abstract

“…This approach allows applying the smoothing along the structural features represented in the guiding image and then providing models with structural information that are consistent with known guiding images. The guiding image can come from any image bearing such structural information (see [9,46] for some recent examples). In Fig.…”

Section: Inverse Problemmentioning

confidence: 99%

Image-guided inversion in steady-state hydraulic tomography

Ahmed

Zhou

Jardani

et al. 2015

Advances in Water Resources

View full text Add to dashboard Cite

a b s t r a c tIn steady-state hydraulic tomography, the head data recorded during a series of pumping or/and injection tests can be inverted to determine the transmissivity distributions of an aquifer. This inverse problem is usually under-determined and ill-posed. We propose to use structural information inferred from a guiding image to constrain the inversion process. The guiding image can be drawn from soft data sets such as seismic and ground penetrating radar sections or from geological cross-sections inferred from the wells and some geological expertise. The structural information is extracted from the guiding image through some digital image analysis techniques. Then, it is introduced into the inversion process of the head data as a weighted four direction smoothing matrix used in the regularizer. Such smoothing matrix allows applying the smoothing along the structural features. This helps preserving eventual drops in the hydraulic properties. In addition, we apply a procedure called image-guided interpolation. This technique starts with the tomogram obtained from the image-guided inversion and focus this tomogram. These new approaches are applied on four synthetic toy problems. The hydraulic distributions estimated from the image-guided inversion are closer to the true transmissivity model and have higher resolution than those computed from a classical Gauss-Newton method with uniform isotropic smoothing.

show abstract

Field characterization and data integration to define the hydraulic heterogeneity of a shallow granular aquifer at a sub-watershed scale

et al. 2014

View full text Add to dashboard Cite

The need to adapt the exploration model from the oil patch to contaminated-site characterization: A case from Hill AFB, Utah, USA

Cited by 4 publications

References 10 publications

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

Image-guided inversion in steady-state hydraulic tomography

Field characterization and data integration to define the hydraulic heterogeneity of a shallow granular aquifer at a sub-watershed scale

Contact Info

Product

Resources

About