Global Sensitivity Analysis to Optimize Basin-Scale Conductive Model Calibration – A Case Study From the Upper Rhine Graben

Degen, Denise; Veroy, Karen; Freymark, Jessica; Scheck‐Wenderoth, Magdalena; Wellmann, Florian

doi:10.31223/osf.io/b7pgs

Cited by 7 publications

(27 citation statements)

References 48 publications

(83 reference statements)

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“…We perform the SA with the Python library SALib (Herman and Usher, 2017) and 100,000 realizations per parameter to reduce the statistical error. For further information regarding the global sensitivity analysis refer to Sobol (2001); Saltelli (2002); Saltelli et al (2010), and for a comparison between local and global sensitivity analysis to Wainwright et al (2014) and Degen et al (2020a).…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

“…For further information regarding the RB method refer to Hesthaven et al (2016);Prud'homme et al (2002); Quarteroni et al (2015) and a detailed overview of various model order reduction techniques is provided in Benner et al (2015). Further information regarding the RB method in the field of Geosciences is presented by Degen et al (2020b) and specifically for basin-scale thermal applications in (Degen et al, 2020a).…”

Section: Reduced Order Modelingmentioning

confidence: 99%

“…The consequences of introducing a weighting scheme have been already partly addressed in Degen et al (2020a). However, there the authors focused on the consequences for the process of model calibrations.…”

Section: Influence Of the Weightingmentioning

confidence: 99%

“…Model calibration aims to compensate for existing model errors by adjusting the model parameters in accordance with our temperature measurements. Analogous to Degen et al (2020a), we use a sensitivity-driven model calibration for more robust can be calibrated are yielded (Table 1). The data is insensitive to the remaining parameters.…”

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confidence: 99%

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How biased are our models? – A Case Study of the Alpine Region

Degen

Spooner

Scheck‐Wenderoth

et al. 2021

Preprint

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Abstract. Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.

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Section: Global Sensitivity Analysismentioning

confidence: 99%

Section: Reduced Order Modelingmentioning

confidence: 99%

Section: Influence Of the Weightingmentioning

confidence: 99%

mentioning

confidence: 99%

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How biased are our models? – A Case Study of the Alpine Region

Degen

Spooner

Scheck‐Wenderoth

et al. 2021

Preprint

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“…The RB method is a model order reduction (MOR) technique that aims at significantly reducing the spatial and temporal degrees of freedom of, as applied in this study, finite element problem formulations. The RB method has been widely studied by, for example, Grepl and Patera (2005), Hesthaven et al (2016), Prud'homme et al (2002 and Quarteroni et al (2015) for mathematical benchmark examples and for the first time by Degen et al (2020c) in a geoscientific context. In contrast to other statistical methods including kriging and response surfaces (Baş and Boyacı, 2007;Bezerra et al, 2008;Frangos et al, 2010;Khuri and Mukhopadhyay, 2010;Miao et al, 2019;Mo et al, 2019;Myers et al, 2016;Navarro et al, 2018), the RB method enables the retrieval of the entire state variable (i.e.…”

Section: Introductionmentioning

confidence: 99%

Effects of transient processes for thermal simulations of the Central European Basin

Degen

Cacace

2021

Geosci. Model Dev.

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Abstract. Transient processes play a major role in geophysical applications. In this paper, we quantify the significant influence arising from transient processes for conductive heat transfer problems for sedimentary basin systems. We demonstrate how the thermal properties are affected when changing the system from a stationary to a non-stationary (transient) state and what impact time-dependent boundary conditions (as derived from paleoclimate information) have on the system's overall response. Furthermore, we emphasize the importance of the time-stepping approach adopted to numerically solve for the transient case and the overall simulation duration since both factors exert a direct influence on the sensitivities of the thermal properties. We employ global sensitivity analyses to quantify not only the impact arising from the thermal properties but also their parameter correlations. Furthermore, we showcase how the results of such sensitivity analysis can be used to gain further insights into the complex Central European Basin System in central and northern Europe. This computationally very demanding workflow becomes feasible through the construction of high-precision surrogate models based on the reduced basis (RB) method.

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Crustal-scale thermal models: revisiting the influence of deep boundary conditions

et al. 2022

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The societal importance of geothermal energy is significantly increasing because of its low carbon-dioxide footprint. However, geothermal exploration is also subject to high risks. For a better assessment of these risks, extensive parameter studies are required that improve the understanding of the subsurface. This yields computationally demanding analyses. Often, this is compensated by constructing models with a small vertical extent. This paper demonstrates that this leads to entirely boundary-dominated and hence uninformative models. It demonstrates the indispensable requirement to construct models with a large vertical extent to obtain informative models with respect to the model parameters. For this quantitative investigation, global sensitivity studies are essential since they also consider parameter correlations. To compensate for the computationally demanding nature of the analyses, a physics-based machine learning approach is employed, namely the reduced basis method, instead of reducing the physical dimensionality of the model. The reduced basis method yields a significant cost reduction while preserving the physics and a high accuracy, thus providing a more efficient alternative to considering, for instance, a small vertical extent. The reduction of the mathematical instead of physical space leads to less restrictive models and, hence, maintains the model prediction capabilities. The combination of methods is used for a detailed investigation of the influence of model boundary settings in typical regional-scale geothermal simulations and highlights potential problems.

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Global Sensitivity Analysis to Optimize Basin-Scale Conductive Model Calibration – A Case Study From the Upper Rhine Graben

Cited by 7 publications

References 48 publications

How biased are our models? – A Case Study of the Alpine Region

How biased are our models? – A Case Study of the Alpine Region

Effects of transient processes for thermal simulations of the Central European Basin

Crustal-scale thermal models: revisiting the influence of deep boundary conditions

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