Anis MOHAMMEDI2025-01-212025-01-212025-01-21https://dspace.estin.dz/handle/123456789/29Accurate identification and delineation of Salt Domes from seismic images play a critical role in geological studies and resource exploration. How ever, traditional methods often struggle with complex geological structures and require extensive manual intervention. This work addresses these chal lenges by proposing a deep learning-based approach for semantic segmen tation of Salt Domes. We introduce novel techniques including Transform ers for spatial context aggregation, U-Net for precise feature extraction, and VAE-Liquid layers for enhanced representation learning. Through rigorous experimentation and evaluation on real-world datasets, we demonstrate the effectiveness of our approach in automating and improving the accuracy of Salt Dome identification. This work contributes to advancing automated ge ological analysis, offering insights into subsurface structures vital for both exploration and hazard assessmentenSemantic SegmentationSeismic ImagesSalt DomesResource ExplorationDeep LearningTransformersU-NetVAE-Liquid LayersThesis