Deep Learning Segmentation of Seismic Facies Based on Proximity Constraint Strategy: Innovative Application of UMA-Net Model

Intelligent seismic facies segmentation has recently gained significant attention, particularly with the application of deep learning technologies. However, existing methods face considerable challenges when processing complex seismic data, often due to their reliance on simplistic image mapping that fails to capture intricate geological structures and spatial relationships. Unlike natural image segmentation, seismic facies segmentation requires a global perspective that accounts for these complexities. To address these limitations, we propose the UMA-Net model, which integrates a U-shaped encoder-decoder structure with a mix-transformer (MiT) and an attention-enhanced convolution module (AECM) to enhance global feature extraction and seismic information acquisition. A key innovation of this study is the proximity constraint strategy (PCS), which shifts the focus from traditional mapping to predicting geological targets by analyzing variations in adjacent seismic data. This approach significantly improves phase boundary identification and reduces phase confusion, offering a novel solution to the challenges of seismic facies segmentation. Experimental results demonstrate that UMA-Net outperforms state-of-the-art networks in metrics such as pixel accuracy (PA) and intersection over union (IoU). Applied to seismic data from the F3 Netherlands work area, UMA-Net enhances segmentation accuracy while reducing reliance on labeled datasets, making it applicable to other regions facing similar geological challenges. This study not only advances the accuracy and efficiency of seismic interpretation but also opens new avenues for deep learning applications in seismic facies segmentation, particularly in improving model generalization across diverse geological settings.