Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China

Miao, Tiansheng; Lu, Wenxi; Jiang, Lin; Guo, Jian; Liu, Tianliang

doi:10.1007/s12517-018-4128-8

Cited by 70 publications

(24 citation statements)

References 27 publications

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“…We investigated the changes in the entire model that is the reason why we use the reduced basis method to construct our surrogate model. In contrast to other surrogate models, it is not restricted to the observation space (Miao et al, 2019;Mo et al, 2019). We performed in total 12,000,000 steady-state forward simulations yielding with an average compute time varying between 1 ms and 4.2 ms for a single forward simulation of the various model scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…This is also the main reason behind our choice to employ the RB method for the surrogate model construction. Many other methods (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) construct surrogate models for the observation space only. This entails that every value outside this space has to be obtained via inter-and extrapolation routines, Therefore these approaches share the disadvantage of not taking the physical laws describing the process of interest into consideration.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

“…The RB method has been widely studied by, for example, Grepl and Patera (2005);Hesthaven et al (2016);Prud'homme et al (2002); Quarteroni et al (2015) for mathematical benchmark examples, and for the first time by Degen et al (2020b) in a geoscientific context. In this study, we make use of the RB method to guide the construction of the surrogate model since it allows, 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 retrieval of the entire state variable (i.e. temperature).…”

Section: Introductionmentioning

confidence: 99%

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Effects of Transient Processes for Thermal Simulations of the Central European Basin

Degen¹,

Cacace²

2020

Preprint

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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 by changing the system from a stationary to a non-stationary system and what impact transient boundary conditions (derived from paleoclimate information) have. Furthermore, we emphasize the importance of time-stepping approaches and the simulation duration since both factors influence 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 show how the results of the sensitivity analysis can be used to gain further insights into the complex Central European Basin System. This computationally very demanding workflow becomes feasible through the construction of high precision surrogate models using the reduced basis method.

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Section: Discussionmentioning

confidence: 99%

Section: Global Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of Transient Processes for Thermal Simulations of the Central European Basin

Degen¹,

Cacace²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…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. temperature).…”

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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“…Hence, we need a surrogate model for the entire state. The reduced basis method is able to provide us with this, in contrast to many other surrogate model techniques (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 reduced basis method is widely known in mathematical applications (i.e.…”

mentioning

confidence: 99%

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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Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China

Cited by 70 publications

References 27 publications

Effects of Transient Processes for Thermal Simulations of the Central European Basin

Effects of Transient Processes for Thermal Simulations of the Central European Basin

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

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

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