On uncertainty quantification in hydrogeology and hydrogeophysics
Recent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will alway...
| Main Authors: | , , , , |
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| Format: | Journal article |
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Elsevier
2017
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| _version_ | 1797078023021789184 |
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| author | Linde, N Ginsbourger, D Irving, J Nobile, F Doucet, A |
| author_facet | Linde, N Ginsbourger, D Irving, J Nobile, F Doucet, A |
| author_sort | Linde, N |
| collection | OXFORD |
| description | Recent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will always remain imprecise, which calls for appropriate uncertainty quantification (UQ). In this paper, we outline selected methodological developments together with pioneering UQ applications in hydrogeology and hydrogeophysics. The applied mathematics and statistics literature is not easy to penetrate and this review aims at helping hydrogeologists and hydrogeophysicists to identify suitable approaches for UQ that can be applied and further developed to their specific needs. To bypass the tremendous computational costs associated with forward UQ based on full-physics simulations, we discuss proxy-modeling strategies and multi-resolution (Multi-level Monte Carlo) methods. We consider Bayesian inversion for non-linear and non-Gaussian state-space problems and discuss how Sequential Monte Carlo may become a practical alternative. We also describe strategies to account for forward modeling errors in Bayesian inversion. Finally, we consider hydrogeophysical inversion, where petrophysical uncertainty is often ignored leading to overconfident parameter estimation. The high parameter and data dimensions encountered in hydrogeological and geophysical problems make UQ a complicated and important challenge that has only been partially addressed to date. |
| first_indexed | 2024-03-07T00:26:29Z |
| format | Journal article |
| id | oxford-uuid:7e506a02-0576-4966-b674-25ac0e8a459d |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:26:29Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:7e506a02-0576-4966-b674-25ac0e8a459d2022-03-26T21:09:25ZOn uncertainty quantification in hydrogeology and hydrogeophysicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7e506a02-0576-4966-b674-25ac0e8a459dSymplectic Elements at OxfordElsevier2017Linde, NGinsbourger, DIrving, JNobile, FDoucet, ARecent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will always remain imprecise, which calls for appropriate uncertainty quantification (UQ). In this paper, we outline selected methodological developments together with pioneering UQ applications in hydrogeology and hydrogeophysics. The applied mathematics and statistics literature is not easy to penetrate and this review aims at helping hydrogeologists and hydrogeophysicists to identify suitable approaches for UQ that can be applied and further developed to their specific needs. To bypass the tremendous computational costs associated with forward UQ based on full-physics simulations, we discuss proxy-modeling strategies and multi-resolution (Multi-level Monte Carlo) methods. We consider Bayesian inversion for non-linear and non-Gaussian state-space problems and discuss how Sequential Monte Carlo may become a practical alternative. We also describe strategies to account for forward modeling errors in Bayesian inversion. Finally, we consider hydrogeophysical inversion, where petrophysical uncertainty is often ignored leading to overconfident parameter estimation. The high parameter and data dimensions encountered in hydrogeological and geophysical problems make UQ a complicated and important challenge that has only been partially addressed to date. |
| spellingShingle | Linde, N Ginsbourger, D Irving, J Nobile, F Doucet, A On uncertainty quantification in hydrogeology and hydrogeophysics |
| title | On uncertainty quantification in hydrogeology and hydrogeophysics |
| title_full | On uncertainty quantification in hydrogeology and hydrogeophysics |
| title_fullStr | On uncertainty quantification in hydrogeology and hydrogeophysics |
| title_full_unstemmed | On uncertainty quantification in hydrogeology and hydrogeophysics |
| title_short | On uncertainty quantification in hydrogeology and hydrogeophysics |
| title_sort | on uncertainty quantification in hydrogeology and hydrogeophysics |
| work_keys_str_mv | AT linden onuncertaintyquantificationinhydrogeologyandhydrogeophysics AT ginsbourgerd onuncertaintyquantificationinhydrogeologyandhydrogeophysics AT irvingj onuncertaintyquantificationinhydrogeologyandhydrogeophysics AT nobilef onuncertaintyquantificationinhydrogeologyandhydrogeophysics AT douceta onuncertaintyquantificationinhydrogeologyandhydrogeophysics |