Supervised pre-stack seismic reflection pattern analysis based on physics-attribute guidance and active learning data augmentation

Seismic reflection pattern analysis or seismic facies analysis is crucial for subsurface reservoir prediction. Supervised pre-stack reflection pattern analysis using well-logging data can fully utilize abundant reservoir information in pre-stack data, and provide clearer physical interpretations than unsupervised methods. However, pre-stack seismic data are high-dimensional, and the well-logging data labels are limited. Traditional convolutional neural network-based approaches face challenges in capturing long-range dependencies across different angle gathers in pre-stack seismic data due to the limitations of their receptive fields. Additionally, existing data augmentation methods lack constraints and physical guidance. To tackle these problems, we introduce a supervised pre-stack seismic reflection pattern analysis method based on the ConvNext network and incorporating physics-guided and active learning for label augmentation. The ConvNext model incorporates the large-kernel attention mechanism, enhancing the model's sensitivity to stratigraphic and spatial features in pre-stack seismic data. To reduce model ambiguity, we develop a label augmentation algorithm that combines active learning and physical attributes. The experiments on synthetic data and real data demonstrate that our method has better performance than the traditional approaches in pre-stack seismic reflection analysis.