Automatic Seismic Facies Segmentation via Wavelet Scattering Transform and Convolutional Network

Seismic facies analysis plays a crucial role in reservoir evaluation and enhancing exploration accuracy. Deep learning (DL) has demonstrated strong potential for fast, accurate, and automated seismic segmentation. However, DL-based methods typically require a large amount of labeled training data, which often lacks physical interpretability. To address these challenges, we propose a hybrid automatic seismic facies classification approach that combines the wavelet scattering transform (WST) with DL, named WSTSFNet. The WST, a time-frequency (TF) method, can be viewed as a type of convolutional neural network (CNN) that employs fixed wavelets as filters, enhancing the model's feature representation, robustness, and generalization capabilities. Additionally, we adopt a modified U-Net as the backbone to establish a nonlinear mapping between 3-D seismic data and facies. To further improve segmentation accuracy, we integrate a holistically nested module in the proposed model. Finally, the proposed WSTSFNet is applied to the Netherlands F3 Block data for performance evaluation. The field data results reveal that the proposed WSTSFNet performs better compared to the comparison networks.