Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910555716976640 |
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| author | Feng, Wen Li, Yong Liu, Yingtian Li, Huating |
| author_facet | Feng, Wen Li, Yong Liu, Yingtian Li, Huating |
| contents | Seismic impedance inversion is a widely used technique for reservoir characterization. Accurate, high-resolution seismic impedance data form the foundation for subsequent reservoir interpretation. Deep learning methods have demonstrated significant potential in seismic impedance inversion. Traditional single semi-supervised networks, which directly input original seismic logging data, struggle to capture high-frequency weak signals. This limitation leads to low-resolution inversion results with inadequate accuracy and stability. Moreover, seismic wavelet uncertainty further constrains the application of these methods to real seismic data. To address these challenges, we propose ADDIN-I: an Attention-based Dual-branch Double-Inversion Network for Impedance prediction. ADDIN-I's dual-branch architecture overcomes the limitations of single-branch semi-supervised networks and improves the extraction of high-frequency weak signal features in sequence modeling. The network incorporates an attention mechanism to further enhance its feature extraction capabilities. To adapt the method for real seismic data applications, a deep learning forward operator is employed to fit the wavelet adaptively. ADDIN-I demonstrates excellent performance in both synthetic and real data applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network Feng, Wen Li, Yong Liu, Yingtian Li, Huating Geophysics Seismic impedance inversion is a widely used technique for reservoir characterization. Accurate, high-resolution seismic impedance data form the foundation for subsequent reservoir interpretation. Deep learning methods have demonstrated significant potential in seismic impedance inversion. Traditional single semi-supervised networks, which directly input original seismic logging data, struggle to capture high-frequency weak signals. This limitation leads to low-resolution inversion results with inadequate accuracy and stability. Moreover, seismic wavelet uncertainty further constrains the application of these methods to real seismic data. To address these challenges, we propose ADDIN-I: an Attention-based Dual-branch Double-Inversion Network for Impedance prediction. ADDIN-I's dual-branch architecture overcomes the limitations of single-branch semi-supervised networks and improves the extraction of high-frequency weak signal features in sequence modeling. The network incorporates an attention mechanism to further enhance its feature extraction capabilities. To adapt the method for real seismic data applications, a deep learning forward operator is employed to fit the wavelet adaptively. ADDIN-I demonstrates excellent performance in both synthetic and real data applications. |
| title | Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network |
| topic | Geophysics |
| url | https://arxiv.org/abs/2408.02524 |