<p>Seismic texture analysis is crucial for geological interpretation, particularly in identifying complex subsurface structures such as salt domes, which are significant both as hydrocarbon traps and drilling hazards. Traditional texture attributes—such as those based on the GLCM and chaos have been widely used for salt dome detection but often encounter limitations in accurately determining the lateral boundaries and bottoms of salt domes. This paper introduces a suite of entropy-based texture attributes, including Shannon, Rényi, Tsallis, Lempel–Ziv, sample, fuzzy, permutation, distribution, and dispersion entropy measures, specifically adapted for the identification of salt domes in 2D seismic data. These entropy measures quantify the complexity, randomness, and structural irregularity inherent in seismic images, thereby providing a more robust characterization of salt dome textures compared to conventional methods. The proposed approach eliminates the dependency on dip-guided moving windows, enhancing computational efficiency and reducing sensitivity to dip estimation errors. Quantitative evaluation using the DB-index on both synthetic and real seismic datasets demonstrates that the entropy-based attributes offer superior discrimination of salt domes, outperforming both GLCM-based and chaos-based attributes in terms of accuracy. The results confirm that entropy-driven texture analysis significantly improves the precision and reliability of salt dome delineation, thereby supporting more effective geological modeling and risk assessment in exploration geophysics.</p>