Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction
Abstract Natural gas remains the only fossil energy with sustained production growth amid global carbon reduction efforts, with seismic methods crucial for its exploration and development. Traditional seismic inversion methods, which detect gas indirectly by modeling and inverting gas‐sensitive para...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2025JH000631 |
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| author | Long Han Xinming Wu Renjie Chen Yunhua Shi Zhanxuan Hu Huijing Fang |
| author_facet | Long Han Xinming Wu Renjie Chen Yunhua Shi Zhanxuan Hu Huijing Fang |
| author_sort | Long Han |
| collection | DOAJ |
| description | Abstract Natural gas remains the only fossil energy with sustained production growth amid global carbon reduction efforts, with seismic methods crucial for its exploration and development. Traditional seismic inversion methods, which detect gas indirectly by modeling and inverting gas‐sensitive parameters, can introduce cumulative errors, whereas conventional deep learning methods struggle with few‐shot generalization due to limited labeled data. This paper presents a deep learning workflow for directly predicting gas saturation from multiple seismic attribute data, involving pre‐training on large‐scale unlabeled data and finetuning with few labeled data to address the few‐shot challenge. Our network includes a Depthwise CNN1D for multi‐attribute feature extraction, an iTransformer for feature fusion, and a predictor for outputting the target. We use the iTransformer's self‐attention mechanism to calculate attribute weights for selection, and through extensive experiments, we developed a windowed multi‐attribute data input method that incorporates neighboring information to ensure lateral consistency. Based on the geological understanding that different attributes computed from the same sample convey correlated features reflecting geological properties, we pre‐train the network on large‐scale unlabeled data using a self‐supervised learning strategy of attribute masking and recovery. This approach encourages the network to learn correlation features, thereby improving its generalization by familiarizing it with the data distribution across the entire study field. We then employ the LoRA finetuning method to adapt the pre‐trained model to gas saturation prediction with meager labeled data while preserving pre‐trained knowledge. We applied this method to field data in the South China Sea, achieving accurate and generalized gas saturation predictions. |
| format | Article |
| id | doaj-art-caf64762315a4ee18226d7ab2ad7aa00 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-caf64762315a4ee18226d7ab2ad7aa002025-08-20T03:27:22ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000631Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing PredictionLong Han0Xinming Wu1Renjie Chen2Yunhua Shi3Zhanxuan Hu4Huijing Fang5School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaSchool of Earth and Space Sciences University of Science and Technology of China Hefei ChinaShenzhen Branch of CNOOC China Limited Shenzhen ChinaShenzhen Branch of CNOOC China Limited Shenzhen ChinaSchool of Information Science and Technology Yunnan Normal University Kunming ChinaExploration Research Institute Anhui Provincial Bureau of Coal Geology Hefei ChinaAbstract Natural gas remains the only fossil energy with sustained production growth amid global carbon reduction efforts, with seismic methods crucial for its exploration and development. Traditional seismic inversion methods, which detect gas indirectly by modeling and inverting gas‐sensitive parameters, can introduce cumulative errors, whereas conventional deep learning methods struggle with few‐shot generalization due to limited labeled data. This paper presents a deep learning workflow for directly predicting gas saturation from multiple seismic attribute data, involving pre‐training on large‐scale unlabeled data and finetuning with few labeled data to address the few‐shot challenge. Our network includes a Depthwise CNN1D for multi‐attribute feature extraction, an iTransformer for feature fusion, and a predictor for outputting the target. We use the iTransformer's self‐attention mechanism to calculate attribute weights for selection, and through extensive experiments, we developed a windowed multi‐attribute data input method that incorporates neighboring information to ensure lateral consistency. Based on the geological understanding that different attributes computed from the same sample convey correlated features reflecting geological properties, we pre‐train the network on large‐scale unlabeled data using a self‐supervised learning strategy of attribute masking and recovery. This approach encourages the network to learn correlation features, thereby improving its generalization by familiarizing it with the data distribution across the entire study field. We then employ the LoRA finetuning method to adapt the pre‐trained model to gas saturation prediction with meager labeled data while preserving pre‐trained knowledge. We applied this method to field data in the South China Sea, achieving accurate and generalized gas saturation predictions.https://doi.org/10.1029/2025JH000631self‐supervised learningfew‐shot problemgas saturation predictiondeep learningseismic attributes |
| spellingShingle | Long Han Xinming Wu Renjie Chen Yunhua Shi Zhanxuan Hu Huijing Fang Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction Journal of Geophysical Research: Machine Learning and Computation self‐supervised learning few‐shot problem gas saturation prediction deep learning seismic attributes |
| title | Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction |
| title_full | Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction |
| title_fullStr | Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction |
| title_full_unstemmed | Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction |
| title_short | Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction |
| title_sort | self supervised pre training and few shot finetuning for gas bearing prediction |
| topic | self‐supervised learning few‐shot problem gas saturation prediction deep learning seismic attributes |
| url | https://doi.org/10.1029/2025JH000631 |
| work_keys_str_mv | AT longhan selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction AT xinmingwu selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction AT renjiechen selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction AT yunhuashi selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction AT zhanxuanhu selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction AT huijingfang selfsupervisedpretrainingandfewshotfinetuningforgasbearingprediction |