Using Training Images to Build Model Ensembles with Structural Variability

Pirot, Guillaume

doi:10.1111/gwat.12556

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Developing model diversity requires that we use sparse data to constrain our models without overprescribing them to resemble a single most‐likely model (de Pasquale ; Pirot ) and with an appropriate level of model complexity (Guthke ). Developing consequential model diversity requires that we actively seek models that may predict those outcomes of concern (Peeters ; White ).…”

Section: Building a Team Of Rival Modelsmentioning

confidence: 99%

Revisiting the Relationship Between Data, Models, and Decision‐Making

Ferré

2017

Groundwater

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We hydrologists can do a better job of supporting water-resources decision-making. I will argue that we can do this by recognizing that decision makers use qualitative, multiple-narrative approaches. So, rather than providing single-model predictions with quantitative uncertainties, we should develop teams of rival models that inform decision makers about what is known, what is possible, and what is unknown. This requires that we build ensembles of models that include biased, advocacy models that directly represent stakeholders' interests or concerns. From this inclusive platform, we can speak objectively and clearly about the risks that drive stakeholders' decisions. Furthermore, we will be promoting more appropriate use of the scientific method in making informed water-resources decisions.

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Section: Building a Team Of Rival Modelsmentioning

confidence: 99%

Revisiting the Relationship Between Data, Models, and Decision‐Making

Ferré

2017

Groundwater

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“…8ac) and borehole (Fig. 8d-f) probability grids (e.g., Journel, 2002;Krishnan, 2004;Remy et al, 2014). The first step is then to define the prior probability distribution, P (A), which Figure 6.…”

Section: Case 4 -Borehole Lithology Logsmentioning

confidence: 99%

Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

Barfod

Vilhelmsen

Jørgensen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art multiple-point statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted, Denmark, using the snesim algorithm. The goal is to study how changes to the underlying datasets propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the training image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried out exhaustively, and realizations are compared with each other as well as with the cognitive geological model. The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology, or TI, results in a significant increase in realization uncertainty. The airborne transient electromagnetic SkyTEM data used in this study cover a large portion of the Kasted model area and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparser, and 410 boreholes were present in this study. The borehole lithology logs infer local changes in the immediate vicinity of the boreholes, thus, in areas with a high degree of geological heterogeneity, boreholes only provide limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. The importance of the TI was also studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological settings are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

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