Prestack Seismic Waveform Classification via Physical Knowledge-Guided Disentangled Representation

The interpretability and reliability of pre-stack seismic waveform classification are often hindered by the entanglement of various geological factors in pre-stack seismic waveforms, along with their intrinsic uncertainties. To address the problem, we propose a physical knowledge-guided disentangled representation network for a specific interpretation task of pre-stack seismic waveform classification. Unlike physics-driven deep learning methods that incorporate physical laws directly into network structures or loss functions, our approach uses a physical knowledge constraint network to guide deep feature learning based on physical attributes. This enables the model to capture more interpretable and separable features. First the concept of group supervision and disentangled representation theory is applied to separate specific physical attributes within the pre-stack seismic waveform. Then, physical knowledge about the seismic data is incorporated to impose additional constraints during the disentangling of deep features. Finally, a clustering technique is used to cluster disentangled deep features and generate the clustering result, which can assist geological experts in inferring sedimentary environments and the distribution of reservoir. Testing on synthetic data demonstrates that our method effectively separates and reconstructs geological elements within seismic data, accurately classifies different types of reflection patterns, and shows strong robustness against noise. Furthermore, the application of actual data shows that the classification results of pre-stack seismic waveform using the proposed method have better interpretability.