Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks
文献类型：期刊论文
| 作者 | Tao, Shiping1; Guo, Yintong4; Huang, Haoyong3; Li, Junfeng3; Chen, Liqing3; Gui, Junchuan3; Zhao, Guokai2 |
| 刊名 | PROCESSES
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 12期号:10页码:24 |
| 关键词 | pre-stack seismic inversion deep learning method multi-input strong robustness transfer learning |
| DOI | 10.3390/pr12102259 |
| 英文摘要 | Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface properties. However, most of the current DL-based methods rely solely on seismic data, lacking the incorporation of prior information. In addition, these methods are usually performed trace-by-trace, resulting in insufficient horizontal constraints. These limitations make traditional methods less robust, particularly when dealing with high noise levels or limited data. To address these challenges, we propose a multi-input deep learning network for pre-stack inversion, which combines data-driven and model-driven approaches for optimization. The proposed method separately extracts features from the model and data, merging them to improve feature utilization. Moreover, by adopting a 2-D training unit, rather than a trace-by-trace approach, the method improves the horizontal continuity of the results. Tests on synthetic and real seismic data confirmed the robustness and improved stability of the proposed method, even under challenging conditions. This dual-driven approach significantly enhances the reliability of seismic inversion. |
| 资助项目 | Science and Technology Special Project of PetroChina Company Limited ; [2023ZZ21] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001341892000001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42959]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Zhao, Guokai |
| 作者单位 | 1.Sichuan Shale Gas Explorat & Dev Co Ltd, Chengdu 610051, Peoples R China 2.Chongqing Univ, State Key Lab Coal Mine Disaster Dynam & Control, Chongqing 400044, Peoples R China 3.Gas Res Inst PetroChina Southwest Oil & Gas Field, Chengdu 610051, Peoples R China 4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Shiping,Guo, Yintong,Huang, Haoyong,et al. Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks[J]. PROCESSES,2024,12(10):24. |
| APA | Tao, Shiping.,Guo, Yintong.,Huang, Haoyong.,Li, Junfeng.,Chen, Liqing.,...&Zhao, Guokai.(2024).Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks.PROCESSES,12(10),24. |
| MLA | Tao, Shiping,et al."Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks".PROCESSES 12.10(2024):24. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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