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Supplemental data files for: Joint imaging of ERT datasets and its application in seepage characterization at Nanshan dam, southeast China
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| Type: | Resource | |
| Storage: | The size of this resource is 100.2 KB | |
| Created: | Oct 12, 2021 at 5:35 a.m. (UTC) | |
| Last updated: | Nov 28, 2023 at 6:38 p.m. (UTC) (Metadata update) | |
| Published date: | Nov 28, 2023 at 6:38 p.m. (UTC) | |
| DOI: | 10.4211/hs.3ec89350233f42899b83650775fd1ad8 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 2624 |
| Downloads: | 71 |
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Abstract
Hydrogeophysical techniques, such as electrical resistivity tomography (ERT), significantly enhance our ability to observe fluid transport and transformation within highly heterogeneous subsurface environments, as well as aid in inferring hydrological models. Despite their efficacy, these methods encounter discrepancies and uncertainties related to data acquisition and geophysical inversion. To address these issues, joint inversions emerge as preferred methodologies, aiming to reduce ambiguity and establish a unified earth model. A primary challenge in this approach is the effective integration of prior information into the joint inversion framework. Particularly in cases involving multiple datasets focused on a single physical property (e.g., electrical resistivity), there exists an inherent and intrinsic parameter relationship that links collocated resistivity models, suggesting a consistent subsurface geoelectric structure. Addressing this, we introduce the concept of intrinsic parameter relationship coupling within compositional joint inversion frameworks. This method is applied to field scenarios involving Wenner, Wenner-Schlumberger, and dipole-dipole datasets to delineate preferential seepage pathways. Our observations indicate that the intrinsic parameter relationship coupling scheme effectively resolves discrepancies in data coverage, sensitivity, and Signal-to-Noise Ratio (SNR). This research contributes to the field of hydrogeology by providing more accurate resistivity estimates and distributions, utilizing multiple ERT datasets derived from varied electrode configurations.
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Natural Science Foundation of China | None | 41974115 |
| Zhejiang Provincial Natural Science Foundation of China | None | LY19D040001 |
| National Natural Science Foundation of China | None | 42274188 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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