Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys Article Swipe
Shinhye Kong
,
Jongchan Oh
,
Daeung Yoon
,
Dong-Woo Ryu
,
Hyoung-Seok Kwon
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13106250
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13106250
Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13106250
- https://www.mdpi.com/2076-3417/13/10/6250/pdf?version=1684743242
- OA Status
- gold
- Cited By
- 6
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377091413
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4377091413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13106250Digital Object Identifier
- Title
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Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity SurveysWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-05-19Full publication date if available
- Authors
-
Shinhye Kong, Jongchan Oh, Daeung Yoon, Dong-Woo Ryu, Hyoung-Seok KwonList of authors in order
- Landing page
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https://doi.org/10.3390/app13106250Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/13/10/6250/pdf?version=1684743242Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/13/10/6250/pdf?version=1684743242Direct OA link when available
- Concepts
-
Inversion (geology), Borehole, Workflow, Electrical resistivity and conductivity, Deep learning, Electrical resistivity tomography, Geology, Reservoir modeling, Artificial intelligence, Computer science, Geotechnical engineering, Engineering, Seismology, Electrical engineering, Database, TectonicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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