Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning
Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissoluti...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Natural Gas Industry B |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352854025000221 |
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| author | Hanqing Wang Han Wang Kunyan Liu Jin Meng Yitian Xiao Yanghua Wang |
| author_facet | Hanqing Wang Han Wang Kunyan Liu Jin Meng Yitian Xiao Yanghua Wang |
| author_sort | Hanqing Wang |
| collection | DOAJ |
| description | Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution. Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature. To address these limitations, this study proposes a transfer learning–based strategy for fault identification in deep fault-karst carbonate formations. The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area. These synthetic samples were used to pretrain an improved U-Net network architecture, enhanced with an attention mechanism, to create a robust pretrained model. Subsequently, real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset. This combination of synthetic and real-world data was used to fine-tune the pretrained model, significantly improving its fault interpretation accuracy. The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset. Furthermore, the proposed transfer learning strategy significantly improves fault recognition accuracy. By replacing the traditional weighted cross-entropy loss function with the Dice loss function, the model successfully addresses the issue of extreme class imbalance between positive and negative samples. Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations, providing a novel and effective technical approach for fault interpretation in such complex geological settings. |
| format | Article |
| id | doaj-art-ed8f9eae47814bccaea2b95a1efb6638 |
| institution | OA Journals |
| issn | 2352-8540 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Natural Gas Industry B |
| spelling | doaj-art-ed8f9eae47814bccaea2b95a1efb66382025-08-20T01:48:26ZengKeAi Communications Co., Ltd.Natural Gas Industry B2352-85402025-04-0112217418510.1016/j.ngib.2025.03.006Seismic fault identification of deep fault-karst carbonate reservoir using transfer learningHanqing Wang0Han Wang1Kunyan Liu2Jin Meng3Yitian Xiao4Yanghua Wang5Petroleum Exploration and Production Research Institute, SINOPEC, Beijing, 102206, China; Resource Geophysics Academy, Imperial College London, London SW7 2AZ, UKPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 102206, China; Corresponding author.Petroleum Exploration and Production Research Institute, SINOPEC, Beijing, 102206, ChinaPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 102206, ChinaPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 102206, ChinaResource Geophysics Academy, Imperial College London, London SW7 2AZ, UKSeismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution. Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature. To address these limitations, this study proposes a transfer learning–based strategy for fault identification in deep fault-karst carbonate formations. The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area. These synthetic samples were used to pretrain an improved U-Net network architecture, enhanced with an attention mechanism, to create a robust pretrained model. Subsequently, real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset. This combination of synthetic and real-world data was used to fine-tune the pretrained model, significantly improving its fault interpretation accuracy. The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset. Furthermore, the proposed transfer learning strategy significantly improves fault recognition accuracy. By replacing the traditional weighted cross-entropy loss function with the Dice loss function, the model successfully addresses the issue of extreme class imbalance between positive and negative samples. Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations, providing a novel and effective technical approach for fault interpretation in such complex geological settings.http://www.sciencedirect.com/science/article/pii/S2352854025000221Seismic faultFault-karst carbonateU-NetTransfer learningAttention mechanism |
| spellingShingle | Hanqing Wang Han Wang Kunyan Liu Jin Meng Yitian Xiao Yanghua Wang Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning Natural Gas Industry B Seismic fault Fault-karst carbonate U-Net Transfer learning Attention mechanism |
| title | Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning |
| title_full | Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning |
| title_fullStr | Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning |
| title_full_unstemmed | Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning |
| title_short | Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning |
| title_sort | seismic fault identification of deep fault karst carbonate reservoir using transfer learning |
| topic | Seismic fault Fault-karst carbonate U-Net Transfer learning Attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S2352854025000221 |
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