Construction and Application of a Foundation Model Based on Prestack Seismic Data

Prestack seismic data can reveal subtle differences in reservoir lithology and fluid characteristics, making it invaluable for hydrocarbon exploration. With the advancement of deep learning technologies, intelligent prestack interpretation methods have gained widespread application. However, existing methods generally suffer from two major limitations: first, in feature extraction, current models struggle to adequately capture the complex inter-trace variations and offset-dependent correlations in prestack data, resulting in insufficient information utilization; second, in scenario adaptation, due to the lack of unified foundational feature representations, each new task requires designing and training models from scratch, leading to high computational costs and low generalization efficiency. Foundation model technology, leveraging large-scale pretraining and efficient fine-tuning mechanisms, can establish unified deep feature representations to enhance inter-trace variation modeling capabilities and achieve rapid application adaptation through the pretrain-finetune paradigm. However, existing seismic foundation models primarily target poststack data and face two major challenges when applied to prestack data interpretation: lack of masking mechanisms specifically designed for inter-trace variation characteristics in prestack data; absence of physical constraints in model architectures that emphasize the importance of inter-trace variations. To address these challenges, this paper proposes a foundation model framework specifically designed for prestack seismic data. First, by randomly masking portions of offset data, the framework effectively models inter-trace variation characteristics and their physical correlations. Second, sparse constraint mechanisms are introduced to highlight key physical features such as AVO responses. Finally, lightweight adapter modules are employed to achieve rapid scenario adaptation. Synthetic data experiments validate the effectiveness of the masking reconstruction mechanism and physical constraint modules. Field data validation, using lithology identification and gas-bearing detection as typical applications, demonstrates that the framework achieves favorable results in both applications, providing a unified and efficient solution for intelligent interpretation of prestack seismic data.