porosity; sustainable groundwater management; hydrogeophysics; groundwater remediation; flow and transport modeling; hydrogeology; permeability; regional-scale aquifer characterization; groundwater protection; surficial aquifers; history matching; contaminant transport
Nussbaumer Raphaël, Mariethoz Grégoire, Gloaguen Erwan, Holliger Klaus (2020), Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling, in Geophysical Journal International
, 221(3), 2184-2200.
Nussbaumer Raphaël, Linde Niklas, Mariethoz Grégoire, Holliger Klaus (2019), Simulation of fine-scale electrical conductivity fields using resolution-limited tomograms and area-to-point kriging, in Geophysical Journal International
, 218(2), 1322-1335.
Nussbaumer Raphael, Mariéthoz Grégoire, Gravey Mathieu, Gloaguen Erwan, Holliger Klaus (2018), Accelerating Sequential Gaussian Simulation with a constant path, in Computers & Geosciences
, 112, 121-132.
Nussbaumer Raphael, Mariéthoz Grégoire, Gloaguen Erwan, Holliger Klaus (2018), Which Path to Choose in Sequential Gaussian Simulation, in Mathematical Geosciences
, 50(1), 97-120.
Nussbaumer Raphael, Mariéthoz Grégoire, Gloaguen Erwan, Holliger Klaus (2017), Accurate and efficient integration of geophysical and hydraulic data at the sub-regional scale through Bayesian sequential simulation with log-linear pooling, in SEG Technical Program Expanded Abstracts 2017
, Houston, Texas, USASociety of Exploration Geophysicists, Tulsa, Oklahoma, USA.
Perozzi Lorenzo, Gloaguen Erwan, Giroux Bernard, Holliger Klaus (2016), A stochastic inversion workflow for monitoring the distribution of CO2 injected into deep saline aquifers, in Computational Geosciences
, (6), 1287-1300.
A comprehensive strategy is proposed for the regional-scale characterization of surficial aquifers based on the integration of pertinent geophysical and hydraulic data. The key objective of this research is to develop methodologies that are capable of providing aquifer-scale models of the permeability distribution that allow for a faithful prediction of the pertinent flow and transport phenomena. This type of information is critical for the protection, remediation, and sustainable management of the world’s increasingly scarce and fragile groundwater resources. Geophysical methods have the potential to bridge the inherent gap in terms of spatial resolution and coverage that exists for traditional hydrogeological methods and significant progress has been made with regard to the quantitative integration of geophysical and hydraulic data at the local scale. The extension of such approaches to the regional scale, where by far the largest benefits of correspondingly improved hydraulic models wait to be reaped, has, however, only recently come into focus and still represents a fundamental methodological challenge. This project seeks to address this problem through a stochastic procedure for the integration of the regional-scale geophysical and hydraulic database followed by a refinement of the thus inferred permeability models through a history matching approach. The goal is to develop a comprehensive aquifer characterization approach, which, based on a typical hydrogeophysical database, is capable of generating faithful stochastic realizations of the fine-scale permeability distribution throughout the probed subsurface region and that allows for a rigorous quantification of the associated uncertainty. Since the data integration part has, in its essence, already been developed and tested in the course of a corresponding predecessor project, the current research endeavor will largely focus on the history matching part. At present, we consider sequential and/or nested multi-scale approaches to be the most promising avenues for this purpose. This integrated approach will allow for a comprehensive quantitative interpretation of typical regional-scale hydrogeophysical databases and thus has the potential of providing the most realistic larger-scale aquifer models available to date. In addition to this, we propose to revisit the initial downscaling step data of our data integration algorithm and explore whether and how the exploitation of the information contained in the low-resolution regional-scale geophysical data can be optimized. Currently, this part of the algorithm is responsible for the overwhelming fraction of the total computational cost and hence also offers the greatest potential for substantial gains in efficiency. The anticipated savings will not only allow for compensating the additional computational effort associated with the newly added history matching procedure, but will also contribute substantially towards our longer-term objective of making this novel regional-scale aquifer characterization approach amenable to three-dimensional models.