面向三维地震表示学习与跨区域声波阻抗反演迁移的方向感知掩码预训练
Direction-Aware Masked Pretraining on 3D Seismic Data with Transfer to Cross-Area Acoustic Impedance Inversion
- State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China)（中国石油大学（华东）油气藏地质与开发国家重点实验室）
- College of Computer Science and Technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对三维地震数据，提出方向感知掩码自编码器，通过各向异性分块与沿道掩码提升表示学习，在跨区域声波阻抗反演中较传统方法降低归一化均方根误差20.8%。
AI中文摘要：
大量未标注的三维地震数据为自监督表示学习及后续迁移至声波阻抗反演提供了机会。然而，传统的掩码预训练通常将三个轴等同对待，忽视了横向反射体结构与纵向波形特征之间的差异。我们提出了一种针对三维叠后地震数据的方向感知掩码自编码器，结合了各向异性分块（tokenization）、方向感知表示、沿道对齐的管状掩码（trace-aligned tube masking）以及针对反射体连续性和波形特征设计的重建约束。我们利用野外数据通过掩码重建和跨区域声波阻抗反演评估了重建质量和下游迁移性。重建对横向分块分辨率比对纵向块长度的适度变化更为敏感。在所评估的配置中，增加编码器容量并不能完全弥补因更粗糙分块导致的重建保真度损失。优选的分块尺度和掩码策略在重建与反演之间有所不同，表明重建保真度本身并不能可靠指示迁移性。对于跨区域反演，预训练模型在源区域进行微调，并直接应用于目标区域而无需进一步更新参数。在目标区域井控有限的情况下，与预训练的常规掩码自编码器相比，在匹配的分块和下游设置下，所提出的框架在八个目标区域测试井上将归一化均方根误差降低了20.8%。这些结果证明了方向感知掩码预训练对野外地震反演的价值，并表明分块尺度和掩码策略应根据下游任务需求而非仅依据重建精度来选择。
英文摘要：
Seismic feature extraction and acoustic impedance inversion are important for subsurface characterisation, but sparse well-log data limit the generalisation of data-driven inversion models across different areas. Self-supervised masked pretraining offers a way to exploit large volumes of unlabelled seismic data; however, conventional approaches often overlook the directional characteristics of 3D seismic data. We propose a direction-aware masked autoencoder (DA-MAE) that distinguishes lateral reflector structure from vertical waveform characteristics in 3D post-stack data. The framework incorporates this distinction into token representation, masking geometry, and reconstruction constraints on reflector continuity and waveform fidelity. We evaluate DA-MAE through masked reconstruction on an independent field survey and cross-area acoustic impedance inversion using two additional field areas. Reconstruction experiments show that performance is more sensitive to lateral token resolution than to moderate changes in vertical patch length, and that increasing encoder capacity does not fully compensate for coarse tokenization. Moreover, the preferred token scales and masking strategies vary between reconstruction and inversion, suggesting that reconstruction fidelity alone is not a reliable indicator of transferability. In cross-area inversion, the pretrained representations remain effective in the target area and improve impedance prediction, demonstrating their transferability across different seismic surveys. These results provide practical guidance for seismic-specific masked pretraining and its application to acoustic impedance inversion.
