Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation
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| Format: | Preprint |
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2025
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| _version_ | 1866918072751751168 |
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| author | Cai, Hanpeng Zhang, Haonan Zhang, Liyu Cheng, Suo |
| author_facet | Cai, Hanpeng Zhang, Haonan Zhang, Liyu Cheng, Suo |
| contents | High-resolution processing of seismic signals is crucial for subsurface geological characterization and thin-layer reservoir identification. Traditional high-resolution algorithms can partially recover high-frequency information but often lack robustness, computational efficiency, and consideration of inter-trace structural relationships. Many deep learning methods use end-to-end architectures that do not incorporate prior knowledge or address data domain disparities, leading to limited generalization.To overcome these challenges, this paper presents the Domain-Adaptive Knowledge Distillation Network (DAKD-Net), which integrates a knowledge distillation strategy with a domain adaptation mechanism for high-resolution seismic data processing. Trained on datasets from forward modeling, DAKD-Net establishes physical relationships between low and high-resolution data, extracting high-frequency prior knowledge during a guided phase before detail restoration without prior conditions. Domain adaptation enhances the model's generalization to real seismic data, improving both generalization capability and structural expression accuracy.DAKD-Net employs a U-Net backbone to extract spatial structural information from multi-trace seismic profiles. The knowledge distillation mechanism enables prior knowledge transfer, allowing recovery of high-resolution data directly from low-resolution inputs. Domain-adaptive fine-tuning further enhances the network's performance in actual survey areas. Experimental results show that DAKD-Net outperforms traditional methods and classical deep networks in longitudinal resolution and complex structural detail restoration, demonstrating strong robustness and practicality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_22018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation Cai, Hanpeng Zhang, Haonan Zhang, Liyu Cheng, Suo Geophysics High-resolution processing of seismic signals is crucial for subsurface geological characterization and thin-layer reservoir identification. Traditional high-resolution algorithms can partially recover high-frequency information but often lack robustness, computational efficiency, and consideration of inter-trace structural relationships. Many deep learning methods use end-to-end architectures that do not incorporate prior knowledge or address data domain disparities, leading to limited generalization.To overcome these challenges, this paper presents the Domain-Adaptive Knowledge Distillation Network (DAKD-Net), which integrates a knowledge distillation strategy with a domain adaptation mechanism for high-resolution seismic data processing. Trained on datasets from forward modeling, DAKD-Net establishes physical relationships between low and high-resolution data, extracting high-frequency prior knowledge during a guided phase before detail restoration without prior conditions. Domain adaptation enhances the model's generalization to real seismic data, improving both generalization capability and structural expression accuracy.DAKD-Net employs a U-Net backbone to extract spatial structural information from multi-trace seismic profiles. The knowledge distillation mechanism enables prior knowledge transfer, allowing recovery of high-resolution data directly from low-resolution inputs. Domain-adaptive fine-tuning further enhances the network's performance in actual survey areas. Experimental results show that DAKD-Net outperforms traditional methods and classical deep networks in longitudinal resolution and complex structural detail restoration, demonstrating strong robustness and practicality. |
| title | Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation |
| topic | Geophysics |
| url | https://arxiv.org/abs/2506.22018 |