AI 中文总结
本研究针对现有知识蒸馏框架忽略地震频带关系的问题，提出FADKD-Net框架，通过分解频带特征、针对性蒸馏及跨域对齐，实现低频与高频地震信息的有效连接。
AI 中文摘要
地震数据包含不同频带的丰富信息，其中低频分量主要表征大规模地质结构，高频分量保留精细尺度的地震细节。有效整合这些与频率相关的分量对地震特征学习至关重要，有助于更好地保留结构连续性和精细尺度细节。知识蒸馏为从高质量数据中迁移信息表征提供了有效手段，但现有基于蒸馏的框架通常以全频带方式处理地震特征，忽略了频带间的关系，从而限制了低频与高频知识的协同迁移。为通过知识蒸馏连接低频与高频地震特征，我们提出了频率感知动态知识蒸馏框架（FADKD-Net），该框架建立了师生学习框架，并在低频与高频频带间执行频率感知知识迁移。具体而言，FADKD-Net将地震特征分解为低频与高频分量，并执行针对性蒸馏以利用它们的互补信息。低频蒸馏引导学生模型学习稳定的结构先验，从而提升地震事件的整体连续性；高频蒸馏则增强精细特征建模，提升对复杂小尺度结构的表征能力。此外，我们提出了跨域特征对齐策略，以减少不同勘测间的分布差异，增强FADKD-Net所学习的地震表征的可迁移性。
英文摘要
Seismic data contain rich information across different frequency bands, with low-frequency components primarily characterizing large-scale geological structures and high-frequency components preserving fine-scale seismic details. Effectively integrating these frequency-dependent components is essential for seismic feature learning to better preserve structural continuity and fine-scale details. Knowledge distillation provides an effective means for transferring informative representations from high-quality data. However, existing distillation-based frameworks usually treat seismic features in a full-band manner, ignoring relationships across frequency bands and thereby limiting the coordinated transfer of low- and high-frequency knowledge. To bridge low- and high-frequency seismic features through knowledge distillation, we propose a frequency-aware dynamic knowledge distillation framework (FADKD-Net), which establishes a teacher-student learning framework and performs frequency-aware knowledge transfer between low- and high-frequency bands. Specifically, FADKD-Net decomposes seismic features into low- and high-frequency components and performs targeted distillation to exploit their complementary information. Low-frequency distillation guides the student model to learn stable structural priors, thereby improving the overall continuity of seismic events. Meanwhile, high-frequency distillation enhances detailed feature modeling and improves the representational capability for complex and small-scale structures. Furthermore, a cross-domain feature alignment strategy is proposed to reduce distributional discrepancies across different surveys and enhance the transferability of the seismic representations learned by FADKD-Net.
