Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning
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 i...
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| Hauptverfasser: | , , , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Cambridge University Press
2022-01-01
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| Schriftenreihe: | Data-Centric Engineering |
| Schlagworte: | |
| Online-Zugang: | https://www.cambridge.org/core/product/identifier/S2632673622000326/type/journal_article |
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