Physics-Guided Generative Domain Adaptation for Supervised Seismic Facies Analysis

Seismic facies analysis is crucial for delineating subsurface structures, characterizing reservoirs, and guiding hydrocarbon exploration. Due to the scarcity of labeled field data, supervised learning methods for seismic facies analysis often rely on synthetic datasets for model training. However, this introduces distributional discrepancies between training (synthetic) and inference (field) data, which can degrade model performance in practice. To address this challenge, we propose a physics-guided generative domain adaptation network that generates realistic training data by transferring real seismic characteristics into synthetic data. The model employs a dual-branch architecture: one branch extracts real-world seismic features from field data, while the other generates physically consistent, adapted data from synthetic inputs. We introduce a multi-level feature alignment strategy that progressively aligns features at different network layers in the two branches. By utilizing seismic attributes as geological priors, our approach explicitly distinguishes domain-invariant from domain-specific information, guiding the adaptation process toward physically meaningful transformations rather than purely statistical adjustments. Experimental evaluations on both synthetic and real datasets demonstrate that the data generated by our method closely resemble real seismic data, effectively narrowing the domain gap at the data level. Consequently, training models on the adapted data significantly enhances the accuracy and robustness of supervised seismic facies analysis.