Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data Article Swipe
Kai Gao
,
Carly M. Donahue
,
Bradley G. Henderson
,
Ryan Modrak
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/gji/ggac468
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/gji/ggac468
SUMMARY Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source–receiver observation geometry. We develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/gji/ggac468
- https://academic.oup.com/gji/article-pdf/233/1/448/48352294/ggac468.pdf
- OA Status
- bronze
- Cited By
- 19
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309985422
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309985422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/gji/ggac468Digital Object Identifier
- Title
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Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-26Full publication date if available
- Authors
-
Kai Gao, Carly M. Donahue, Bradley G. Henderson, Ryan ModrakList of authors in order
- Landing page
-
https://doi.org/10.1093/gji/ggac468Publisher landing page
- PDF URL
-
https://academic.oup.com/gji/article-pdf/233/1/448/48352294/ggac468.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
bronzeOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/gji/article-pdf/233/1/448/48352294/ggac468.pdfDirect OA link when available
- Concepts
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Ground-penetrating radar, Remote sensing, Noise (video), Artificial neural network, Computer science, Radar imaging, Radar, Synthetic data, Artificial intelligence, Fidelity, Deep learning, Geology, Computer vision, Image (mathematics), TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 8, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
84Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648 |
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| primary_location.pdf_url | https://academic.oup.com/gji/article-pdf/233/1/448/48352294/ggac468.pdf |
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| primary_location.raw_type | journal-article |
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| primary_location.raw_source_name | Geophysical Journal International |
| primary_location.landing_page_url | https://doi.org/10.1093/gji/ggac468 |
| publication_date | 2022-11-26 |
| publication_year | 2022 |
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