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Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Kreitmair, Monika J. ; Makasis, Nikolas; Menberg, Kathrin ORCID iD icon 1; Bidarmaghz, Asal; Farr, Gareth J.; Boon, David P.; Choudhary, Ruchi
1 Institut für Angewandte Geowissenschaften (AGW), Karlsruher Institut für Technologie (KIT)


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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000152310
Veröffentlicht am 07.11.2022
DOI: 10.1017/dce.2022.32
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2632-6736
KITopen-ID: 1000152310
Erschienen in Data-Centric Engineering
Verlag Cambridge University Press (CUP)
Band 3
Seiten Art.-Nr. e32
Vorab online veröffentlicht am 02.11.2022
Schlagwörter Bayesian calibration, finite element methods, parameter inference, shallow geothermal energy, uncertainty quantification
Nachgewiesen in Scopus
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