Preferential Pathways Inversion From Cross-Borehole Electrical Data
(2024) In Geophysical Research Letters 51(21).- Abstract
Identification of preferential flow-paths, such as fractures, is required for various issues in geosciences. When chemicals are injected into the subsurface, monitoring the resulting structural and chemical changes remains a challenge. The ability of geophysical tomography to tackle this problem is not fully explored due to the lack of numerical methods suitable for modeling narrow structures. We explore how discrete representation of preferential flow-paths provides innovative ways to invert electrical resistivity data collected during reagent injection at a contaminated site. The data set is inverted with a scheme where a new fracture is added at every iteration. This allows identifying newly created narrow conductive structures from... (More)
Identification of preferential flow-paths, such as fractures, is required for various issues in geosciences. When chemicals are injected into the subsurface, monitoring the resulting structural and chemical changes remains a challenge. The ability of geophysical tomography to tackle this problem is not fully explored due to the lack of numerical methods suitable for modeling narrow structures. We explore how discrete representation of preferential flow-paths provides innovative ways to invert electrical resistivity data collected during reagent injection at a contaminated site. The data set is inverted with a scheme where a new fracture is added at every iteration. This allows identifying newly created narrow conductive structures from the field data collected before and after injection. Fracture location remains overall consistent despite using different starting points for the fracture search. A prior constraint on fracture length improves convergence. These results show the potential of discrete inversion for identifying narrow structures from electrical resistivity monitoring.
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- author
- Lelimouzin, Léa ; Champollion, Cédric ; Lévy, Léa LU and Roubinet, Delphine
- organization
- publishing date
- 2024-11-16
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- electrical resistivity, fracture, geophysics, inversion, remediation, software
- in
- Geophysical Research Letters
- volume
- 51
- issue
- 21
- article number
- e2024GL111202
- publisher
- American Geophysical Union (AGU)
- external identifiers
-
- scopus:85208120151
- ISSN
- 0094-8276
- DOI
- 10.1029/2024GL111202
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024. The Author(s).
- id
- 1d66d46a-c9bc-4991-b9de-d04c1dc1d6ec
- date added to LUP
- 2024-11-16 16:16:37
- date last changed
- 2025-04-04 14:48:06
@article{1d66d46a-c9bc-4991-b9de-d04c1dc1d6ec,
abstract = {{<p>Identification of preferential flow-paths, such as fractures, is required for various issues in geosciences. When chemicals are injected into the subsurface, monitoring the resulting structural and chemical changes remains a challenge. The ability of geophysical tomography to tackle this problem is not fully explored due to the lack of numerical methods suitable for modeling narrow structures. We explore how discrete representation of preferential flow-paths provides innovative ways to invert electrical resistivity data collected during reagent injection at a contaminated site. The data set is inverted with a scheme where a new fracture is added at every iteration. This allows identifying newly created narrow conductive structures from the field data collected before and after injection. Fracture location remains overall consistent despite using different starting points for the fracture search. A prior constraint on fracture length improves convergence. These results show the potential of discrete inversion for identifying narrow structures from electrical resistivity monitoring.</p>}},
author = {{Lelimouzin, Léa and Champollion, Cédric and Lévy, Léa and Roubinet, Delphine}},
issn = {{0094-8276}},
keywords = {{electrical resistivity; fracture; geophysics; inversion; remediation; software}},
language = {{eng}},
month = {{11}},
number = {{21}},
publisher = {{American Geophysical Union (AGU)}},
series = {{Geophysical Research Letters}},
title = {{Preferential Pathways Inversion From Cross-Borehole Electrical Data}},
url = {{http://dx.doi.org/10.1029/2024GL111202}},
doi = {{10.1029/2024GL111202}},
volume = {{51}},
year = {{2024}},
}