Improving Bayesian Evidential Learning 1D imaging (BEL1D) accuracy through iterative prior resampling

Michel, Hadrien; Nguyen, Frédéric; Hermans, Thomas

doi:10.1002/essoar.10501770.1

Cited by 2 publications

(4 citation statements)

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“…Recent advances have shown that BEL can also estimate the model parameter distributions and be used as a more traditional inversion technique (Yin et al, 2020;Michel et al, 2020a). However, such more advanced applications require further development of appropriate tools to identify highly non-linear relationships (Park and Caers, 2020) which will inevitably come at a larger computational cost (Michel et al, 2020b). Although these recent developments are promising solutions to integrate large 4D data sets within efficient simulation and inversion framework to forecast the behavior of aquifers, they still need to be more widely evaluated and used, including for complex field cases.…”

Section: Numerical Methods Development For 4d Data Integration and In...mentioning

confidence: 99%

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology

Hermans

Goderniaux

Jougnot

et al. 2022

Preprint

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Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability needs to be considered when studying hydrogeological processes in order to employ adequate mechanistic models or perform upscaling. The scale at which a hydrogeological system should be characterized in terms of its spatial heterogeneity and temporal dynamics depends on the studied process and it is not always necessary to consider the full complexity. In this paper, we identify a series of hydrogeological processes for which an approach coupling the monitoring of spatial and temporal variability, including 4D imaging, is often necessary: (1) groundwater fluxes that control (2) solute transport, mixing and reaction processes, (3) vadose zone dynamics, and (4) surface-subsurface water interaction occurring at the interface between different subsurface compartments. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. Then, we highlight some recent innovations that have led to significant breakthroughs in this domain. We finally discuss how spatial and temporal fluctuations affect our ability to accurately model them and predict their behavior. We thus advocate a more systematic characterization of the dynamic nature of subsurface processes, and the harmonization of open databases to store hydrogeological data sets in their four-dimensional components, for answering emerging scientific question and addressing key societal issues.

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Section: Numerical Methods Development For 4d Data Integration and In...mentioning

confidence: 99%

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology

Hermans

Goderniaux

Jougnot

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Popular rules of thumb include the Silverman's rule of thumb (Silverman, 1986) and the Scott's rule of thumb (Scott, 1992). In addition, Michel et al (2020a) proposed estimating the bandwidth by using the number of training samples in the vicinity of the CHAPTER 2. METHODOLOGY observed data in order to reduce computational expense.…”

Section: Conditional Sampling Of Canonical Variatesmentioning

confidence: 99%

“…Nevertheless, if the linear correlation and normality assumptions in the canonical variate pairs are violated too severely for the application of an analytic MGI (cf. §2.3.1), KDE can be used to approximate p (h|d obs ) instead of MGI (e.g., Hermans et al 2019;Michel et al 2022aMichel et al , 2020a.…”

Section: Conditional Sampling Of Canonical Variatesmentioning

confidence: 99%

“…This limitation can be mitigated in part by applying KDE instead of linear regression (Michel et al, 2020b). Recent research has suggested that the shortcomings of linear CCA can be addressed by modifying the learning procedure and introducing some iterative updating of the prior (Hermans et al, 2019;Michel et al, 2020a;Park and Caers, 2020). However, such adaptation increases the computation cost and reduces the adaptability of BEL as the iterative process is inherently dependent on the dataset, making it less efficient for experimental design.…”

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confidence: 99%

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