ML-assisted intra-salt interpretation of the Zechstein Group in the Norwegian North Sea: Implications for salt caverns

Abstract Salt formations often consist of layered evaporite sequences (LES) with mechanically distinct lithologies, which generate complex internal structures during deformation. Interpreting these structures in seismic data is challenging because of poor imaging, discontinuous reflectors, and strong velocity contrasts. While machine learning (ML) has been increasingly applied to salt boundary detection, its potential for resolving intra-salt structures remains underexplored. This study evaluates the performance of two supervised ML tools—fault prediction and multi-horizon interpretation—across three 3D seismic surveys from the Norwegian North Sea, where the Zechstein salt ranges from bedded salt to salt pillows and diapirs. Results show that ML performance is strongly dependent on seismic image quality and the degree of deformation: bedded salt allows accurate tracking of top, base, and intra-salt horizons, whereas pillows and diapirs exhibit degraded reflector continuity and lower interpretation confidence. Fault prediction not only detects faults but highlights intra-salt discontinuities such as rotated stringers. Confidence maps provide uncertainty-aware interpretations that delineate the zones of higher risk. In the context of subsurface hydrogen storage, the ML-derived confidence maps can guide salt cavern placement and reduce geomechanical risk.