Data-driven selection of the minimum-gradient support parameter in time-lapse focused electric imaging

Nguyen, Frédéric; Kemna, Andreas; Robert, Tanguy; Hermans, Thomas

doi:10.1190/geo2015-0226.1

Cited by 25 publications

(18 citation statements)

References 39 publications

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“…Many authors have observed poor recovery of mass‐balance in tracing experiments [e.g., Singha and Gorelick , ; Müller et al ., ] and over‐ or underestimation of the physical properties due to over‐smoothing of the geophysical image [e.g., Vanderborght et al ., ; Hermans et al ., ]. Some authors have proposed alternative constraints to the standard smoothness constraint in order to reduce smoothing and better estimate physical properties [ Doetsch et al ., ; Fiandaca et al ., ; Caterina et al ., ; Nguyen et al ., ; Hermans et al ., ], but the inherent limitations of deterministic inversion remain.…”

Section: Introductionmentioning

confidence: 99%

Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Hermans

Oware

Caers

2016

Water Resources Research

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Time‐lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction‐focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.

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Section: Introductionmentioning

confidence: 99%

Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Hermans

Oware

Caers

2016

Water Resources Research

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“…Despite the fact that regularization in a time-lapse mode is similar to a static mode, very few studies have investigated the use of alternative spatial regularization operators in time-lapse studies more adapted to the physics of the process (Ajo- Franklin et al, 2007;Doetsch et al, 2010;Fiandaca et al, 2015;Nguyen et al, 2016). Coupled hydrogeophysical inversion schemes are promising.…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, Fiandaca et al (2015) and Nguyen et al (2016) apply the MS and minimum gradient-support (MGS) functionals to time-lapse ERT inversion to produce focused time-lapse changes. This is of particular interest to image preferential flow paths in fractured aquifers or preferential infiltration processes.…”

Section: Introductionmentioning

confidence: 99%

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

2016

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Hydrogeophysics has become a major field of research in the past two decades, and time-lapse electrical resistivity tomography (ERT) is one of the most popular techniques to monitor passive and active processes in shallow subsurface reservoirs. Time-lapse inversion schemes have been developed to refine inversion results, but they mostly still rely on a spatial regularization procedure based on the standard smoothness constraint. We have applied a covariance-based regularization operator to the time-lapse ERT inverse problem. We first evaluated the method for surface and crosshole ERT with two synthetic cases and compared the results with the smoothness-constrained inversion (SCI). These tests showed that the covariance-constrained inversion (CCI) better images the target in terms of shape and amplitude. Although more important in low-sensitivity zones, we have observed improvements everywhere in the tomograms. Those synthetic examples also show that an error made in the range or in the type of the variogram model had a limited impact on the resulting image, which still remained better than SCI. We then applied the method to cross-borehole ERT field data from a heat-tracing experiment, in which the comparison with direct measurements showed a strong improvement of the breakthrough curves retrieved from ERT. This method could be extended to the time dimension, which would allow the use of CCI in 4D inversion schemes.

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“…However, surface measurements provided by P32 were found to have limited spatial resolution due to the presence of artifacts and a high amount of noise. At other industrial sites, a different approach such as time‐lapse inversion of ratio or focused time‐lapse inversion (Rosas Carbajal et al, 2012; Vignoli et al, 2015; Nguyen et al, 2016) might be considered, as they have proven to be successful in avoiding inversion artifacts.…”

Section: Discussionmentioning

confidence: 99%

Tracer Experiment in a Brownfield Using Geophysics and a Vadose Zone Monitoring System

Vera

Beaujean

Jamin

et al. 2017

Vadose Zone Journal

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A saline tracer infiltration test across the fractured vadose zone of an industrial contaminated site in Belgium was monitored by combining surface and cross-borehole electrical resistivity tomography (ERT) methods with a vadose zone monitoring system (VMS). The VMS provides in situ continuous hydraulic and chemical information on the percolating tracer at multiple depths in the vadose zone. The combination of such high-resolution data with timelapse geophysical images that capture the spatiotemporal variability of the subsurface improves interpretations of flow and transport, providing a better characterization of infiltration mechanisms and preferential flow paths. The tracer infiltration test was performed over a heterogeneous vadose zone composed of backfilled materials, sands and silts, and unsaturated fractured chalk. Monitoring results during a 5-d period revealed the formation of a tracer plume in the upper backfilled deposits, while some of the tracer migrated laterally following preferential pathways. Slow vertical flow through matrix pores was found to be dominant under dry conditions. Infiltration of small quantities of rain during the test was found to have an influence on the spatial distribution of the plume. Results from long-term monitoring revealed vertical transport of the tracer toward depths that reached 4 m during a time period of 105 d. During that period, fracture and matrix flow mechanisms across the vadose zone were activated as a response to frequent rainfall episodes. The study demonstrates that the interpretation of geophysical images is improved by in situ information from the VMS.Abbreviations: ERT, electrical resistivity tomography; FTDR, flexible time domain reflectometry; VMS, vadose zone monitoring system; VSP, vadose sampling port.The detailed characterization of the contaminant distribution and percolation mechanisms of industrial contaminated soils is particularly challenging. The presence of disturbed soil, artificial ground, and abandoned infrastructures modifies flow mechanisms in the vadose zone, generating preferential flows that are difficult to identify and quantify. Additionally, temporal and spatial coverage is required to capture the physical and biochemical changes of multiple contaminants in such a dynamic system (Atekwana et al., 2000).Electrical resistivity tomography (ERT) methods are suitable for flow and transport characterization because subsurface electrical properties are influenced by parameters such as solute concentration or water saturation (Slater et al., 2000;Johnson et al., 2013). Electrical resistivity tomography measurements allow monitoring of infiltrated water during precipitation events, which have an impact on contaminant transport across the vadose zone (Descloitres et al., 2008;Clément et al., 2009;Carrière et al., 2015). The combination of ERT methods with tracer tests allows the identification of preferential flow paths and solute transport processes (Ptak et al., 2004;Robert et al., 2012). However, information provided by ERT measurement...

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Data-driven selection of the minimum-gradient support parameter in time-lapse focused electric imaging

Cited by 25 publications

References 39 publications

Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

Tracer Experiment in a Brownfield Using Geophysics and a Vadose Zone Monitoring System

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