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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.23439 |
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| _version_ | 1866914418815664128 |
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| author | Truong, Bao Nguyen, Quang Huang, Baoru Han, Jinpei Nguyen, Van Le, Ngan Pham, Minh-Tan Hien, Doan Huy Nguyen, Anh |
| author_facet | Truong, Bao Nguyen, Quang Huang, Baoru Han, Jinpei Nguyen, Van Le, Ngan Pham, Minh-Tan Hien, Doan Huy Nguyen, Anh |
| contents | Seismic images reconstruct subsurface reflectivity from field recordings, guiding exploration and reservoir monitoring. Gas chimneys are vertical anomalies caused by subsurface fluid migration. Understanding these phenomena is crucial for assessing hydrocarbon potential and avoiding drilling hazards. However, accurate detection is challenging due to strong seismic attenuation and scattering. Traditional physics-based methods are computationally expensive and sensitive to model errors, while deep learning offers efficient alternatives, yet lacks labeled datasets. In this work, we introduce \textbf{SIGMA}, a new physics-based dataset for gas chimney understanding in seismic images, featuring (i) pixel-level gas-chimney mask for detection and (ii) paired degraded and ground-truth image for enhancement. We employed physics-based methods that cover a wide range of geological settings and data acquisition conditions. Comprehensive experiments demonstrate that SIGMA serves as a challenging benchmark for gas chimney interpretation and benefits general seismic understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23439 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images Truong, Bao Nguyen, Quang Huang, Baoru Han, Jinpei Nguyen, Van Le, Ngan Pham, Minh-Tan Hien, Doan Huy Nguyen, Anh Computer Vision and Pattern Recognition Seismic images reconstruct subsurface reflectivity from field recordings, guiding exploration and reservoir monitoring. Gas chimneys are vertical anomalies caused by subsurface fluid migration. Understanding these phenomena is crucial for assessing hydrocarbon potential and avoiding drilling hazards. However, accurate detection is challenging due to strong seismic attenuation and scattering. Traditional physics-based methods are computationally expensive and sensitive to model errors, while deep learning offers efficient alternatives, yet lacks labeled datasets. In this work, we introduce \textbf{SIGMA}, a new physics-based dataset for gas chimney understanding in seismic images, featuring (i) pixel-level gas-chimney mask for detection and (ii) paired degraded and ground-truth image for enhancement. We employed physics-based methods that cover a wide range of geological settings and data acquisition conditions. Comprehensive experiments demonstrate that SIGMA serves as a challenging benchmark for gas chimney interpretation and benefits general seismic understanding. |
| title | SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.23439 |