Image-guided inversion in steady-state hydraulic tomography

Ahmed, A. Soueid; Zhou, Jun; Jardani, Abderrahim; Revil, A.; Dupont, Jean-Paul

doi:10.1016/j.advwatres.2015.04.001

Cited by 26 publications

(14 citation statements)

References 57 publications

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“…It is well‐known that geologic maps portray the spatial distribution of large‐scale geologic features, and geophysical surveys can detect subsurface structures or anomalies [ Soueid Ahmed et al ., ]. Nevertheless, information about such large‐scale features often involves great uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

Zha

Yeh

Illman

et al. 2017

Water Resources Research

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Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site‐specific ones (e.g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site‐specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site‐specific large‐scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometer‐scale‐fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.

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Section: Introductionmentioning

confidence: 99%

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

Zha

Yeh

Illman

et al. 2017

Water Resources Research

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“…Hale [] and Soueid Ahmed et al . [] have proposed the guided image method in which the structural features of the domain is presented graphically and used as a priori information to guide the inversion by refining the model sensitivity at boundaries between different zones. It permits a better estimate of the intrastructure parameter variabilities and location of different features in a model.…”

Section: Introductionmentioning

confidence: 99%

A cellular automata‐based deterministic inversion algorithm for the characterization of linear structural heterogeneities

Fischer

Jardani

Masséi

2017

Water Resources Research

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Inverse problem permits to map the subsurface properties from a few observed data. The inverse problem can be physically constrained by a priori information on the property distribution in order to limit the nonuniqueness of the solution. The geostatistical information is often chosen as a priori information; however, when the field properties present a spatial locally distributed high variability, the geostatistical approach becomes inefficient. Therefore, we propose a new method adapted for fields presenting linear structures (such as a fractured field). The Cellular Automata‐based Deterministic Inversion (CADI) method is, as far as we know when this paper is produced, the first inversion method which permits a deterministic inversion based on a Bayesian approach and using a dynamic optimization to generate different linear structures iteratively. The model is partitioned in cellular automaton subspaces, each one controlling a different zone of the model. A cellular automata subspace structures the properties of the model in two units (“structure” and “background”) and control their dispensing direction and their values. The partitioning of the model in subspaces permits to monitor a large‐scale structural model with only a few pilot‐parameters and to generate linear structures with local direction changes. Thereby, the algorithm can easily handle with large‐scale structures, and a sensitivity analysis is possible on these structural pilot‐parameters, which permits to considerably accelerate the optimization process in order to find the best structural geometry. The algorithm has been successfully tested on simple, to more complex, theoretical models with different inversion techniques by using seismic and hydraulic data.

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“…Generally, aquifer characterization is based on the interpretation of hydraulic observations data collected during pumping, infiltration, or tracer tests (Carrera and Neuman 1986b;Rao et al 2003;Lee and Kitanidis 2014;Pool et al 2015). Therefore, hydraulic tomography is considered as one of the most effective approaches for characterizing the spatial distribution of hydraulic transmissivity of an aquifer (Cardiff et al 2009;Berg and Illman 2013;Cardiff et al 2013;Soueid Ahmed et al 2015;Zha et al 2015;Wang et al 2016). This method relies on a set of hydraulic head responses recorded during cross-hole pumping experiments.…”

Section: Introductionmentioning

confidence: 99%

Application of Large‐Scale Inversion Algorithms to Hydraulic Tomography in an Alluvial Aquifer

Fischer¹,

Jardani

Ahmed

et al. 2016

Groundwater

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Large-scale inversion methods have been recently developed and permitted now to considerably reduce the computation time and memory needed for inversions of models with a large amount of parameters and data. In this work, we have applied a deterministic geostatistical inversion algorithm to a hydraulic tomography investigation conducted in an experimental field site situated within an alluvial aquifer in Southern France. This application aims to achieve a 2-D large-scale modeling of the spatial transmissivity distribution of the site. The inversion algorithm uses a quasi-Newton iterative process based on a Bayesian approach. We compared the results obtained by using three different methodologies for sensitivity analysis: an adjoint-state method, a finite-difference method, and a principal component geostatistical approach (PCGA). The PCGA is a large-scale adapted method which was developed for inversions with a large number of parameters by using an approximation of the covariance matrix, and by avoiding the calculation of the full Jacobian sensitivity matrix. We reconstructed high-resolution transmissivity fields (composed of up to 25,600 cells) which generated good correlations between the measured and computed hydraulic heads. In particular, we show that, by combining the PCGA inversion method and the hydraulic tomography method, we are able to substantially reduce the computation time of the inversions, while still producing high-quality inversion results as those obtained from the other sensitivity analysis methodologies.

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Image-guided inversion in steady-state hydraulic tomography

Cited by 26 publications

References 57 publications

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

A cellular automata‐based deterministic inversion algorithm for the characterization of linear structural heterogeneities

Application of Large‐Scale Inversion Algorithms to Hydraulic Tomography in an Alluvial Aquifer

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