Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning
Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning
复制标题
使用现场数据进行浅层地下建模的贝叶斯参数推断及其对地热规划的影响
DOI:
10.1017/dce.2022.32
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Kreitmair M
中科院分区:
文献类型:
--
作者:
Kreitmair M
Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.
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DOI:
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发表时间:
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期刊:
The Science of the total environment
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--
发表时间:
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