Reference : A cross-validation framework to extract data features for reducing structural uncerta...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/247509
A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity
English
Lopez Alvis, Jorge mailto [Université de Liège - ULiège > Département ArGEnCo > Géophysique appliquée >]
Hermans, Thomas mailto [Universiteit Gent - UGent > Department of Geology > > >]
Nguyen, Frédéric mailto [Université de Liège - ULiège > Département ArGEnCo > Géophysique appliquée >]
Nov-2019
Advances in Water Resources
Elsevier Ltd
133
Yes (verified by ORBi)
03091708
[en] Bayesian hierarchical model ; Dimension reduction ; Feature extraction ; Prior information ; Spatial uncertainty ; Structural uncertainty ; Data mining ; Extraction ; Geological surveys ; Geology ; Ground penetrating radar systems ; Hierarchical systems ; Parameter estimation ; Water management ; Data reduction
[en] Spatial heterogeneity is a critical issue in the management of water resources. However, most studies do not consider uncertainty at different levels in the conceptualization of the subsurface patterns, for example using one single geological scenario to generate an ensemble of realizations. In this paper, we represent the spatial uncertainty by the use of hierarchical models in which higher-level parameters control the structure. Reduction of uncertainty in such higher-level structural parameters with observation data may be done by updating the complete hierarchical model, but this is, in general, computationally challenging. To address this, methods have been proposed that directly update these structural parameters by means of extracting lower dimensional representations of data called data features that are informative and applying a statistical estimation technique using these features. The difficulty of such methods, however, lies in the choice and design of data features, i.e. their extraction function and their dimensionality, which have been shown to be case-dependent. Therefore, we propose a cross-validation framework to properly assess the robustness of each designed feature and make the choice of the best feature more objective. Such framework aids also in choosing the values for the parameters of the statistical estimation technique, such as the bandwidth for kernel density estimation. We demonstrate the approach on a synthetic case with cross-hole ground penetrating radar traveltime data and two higher-level structural parameters: discrete geological scenarios and the continuous preferential orientation of channels. With the best performing features selected according to the cross-validation score, we successfully reduce the uncertainty for these structural parameters in a computationally efficient way. While doing so, we also provide guidelines to design features accounting for the level of knowledge of the studied system. © 2019
Urban and Environmental Engineering, Applied Geophysics
Marie Sklodowska-Curie Actions, ITN, 722028
http://hdl.handle.net/2268/247509
10.1016/j.advwatres.2019.103427
H2020 ; 722028 - ENIGMA - European training Network for In situ imaGing of dynaMic processes in heterogeneous subsurfAce environments

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