Diffraction classification imaging using coordinate attention enhanced DenseNet
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| Publicado en: | Petroleum Science vol. 22, no. 6 (Jun 2025), p. 2353-2384 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
KeAi Publishing Communications Ltd
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3234716698 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1672-5107 | ||
| 022 | |a 1995-8226 | ||
| 024 | 7 | |a 10.1016/j.petsci.2025.03.039 |2 doi | |
| 035 | |a 3234716698 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 100 | 1 | |a Sheng, Tong-Jie |u College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China | |
| 245 | 1 | |a Diffraction classification imaging using coordinate attention enhanced DenseNet | |
| 260 | |b KeAi Publishing Communications Ltd |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In oil and gas exploration, small-scale karst cavities and faults are important targets. The former often serve as reservoir space for carbonate reservoirs, while the latter often provide migration pathways for oil and gas. Due to these differences, the classification and identification of karst cavities and faults are of great significance for reservoir development. Traditional seismic attributes and diffraction imaging techniques can effectively identify discontinuities in seismic images, but these techniques do not distinguish whether these discontinuities are karst cavities, faults, or other structures. It poses a challenge for seismic interpretation to accurately locate and classify karst cavities or faults within the seismic attribute maps and diffraction imaging profiles. In seismic data, the scattering waves are associated with small-scale scatters like karst cavities, while diffracted waves are seismic responses from discontinuous structures such as faults, reflector edges and fractures. In order to achieve classification and identification of small-scale karst cavities and faults in seismic images, we propose a diffraction classification imaging method which classifies diffracted and scattered waves in the azimuth-dip angle image matrix using a modified DenseNet. We introduce a coordinate attention module into DenseNet, enabling more precise extraction of dynamic and azimuthal features of diffracted and scattered waves in the azimuth-dip angle image matrix. Leveraging these extracted features, the modified DenseNet can produce reliable probabilities for diffracted/scattered waves, achieving high-accuracy automatic classification of cavities and faults based on diffraction imaging. The proposed method achieves 96% classification accuracy on the synthetic dataset. The field data experiment demonstrates that the proposed method can accurately classify small-scale faults and scatterers, further enhancing the resolution of diffraction imaging in complex geologic structures, and contributing to the localization of karstic fracture-cavern reservoirs. | |
| 653 | |a Feature extraction | ||
| 653 | |a Imaging techniques | ||
| 653 | |a Kinematics | ||
| 653 | |a Automatic classification | ||
| 653 | |a Accuracy | ||
| 653 | |a Karst | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Azimuth | ||
| 653 | |a Geological structures | ||
| 653 | |a Fault lines | ||
| 653 | |a Oil and gas exploration | ||
| 653 | |a Waves | ||
| 653 | |a Diffraction | ||
| 653 | |a Faults | ||
| 653 | |a Fault detection | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Geology | ||
| 653 | |a Oil exploration | ||
| 653 | |a Wave diffraction | ||
| 653 | |a Seismic response | ||
| 653 | |a Fractures | ||
| 653 | |a Discontinuity | ||
| 653 | |a Reservoirs | ||
| 653 | |a Structures | ||
| 653 | |a Seismic data | ||
| 653 | |a Carbonates | ||
| 653 | |a Synthetic data | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhao, Jing-Tao |u Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, Beijing, 100875, China | |
| 700 | 1 | |a Peng, Su-Ping |u State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology (Beijing), Beijing, 100083, China | |
| 700 | 1 | |a Chen, Zong-Nan |u College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China | |
| 700 | 1 | |a Yang, Jie |u College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China | |
| 773 | 0 | |t Petroleum Science |g vol. 22, no. 6 (Jun 2025), p. 2353-2384 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3234716698/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3234716698/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3234716698/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |