A hybrid approach for salt dome detection in 2D and 3D seismic data
Salt bodies play an important role in subsurface geology, therefore accurate salt dome detection is essential for any seismic interpretation task. Detecting salt body boundary and shape accurately, however, is very difficult due to large noise and amplitude variations in seismic data. Due to the limitations of manual picking, automatic segmentation algorithms are preferred to locate salt domes within the seismic images. In this work, we propose a robust salt dome detection technique based on combined edge and texture attributes. The proposed algorithm overcomes the drawbacks of existing texture attributes based salt dome detection techniques which are heavily dependent upon the relevance of attributes to the geological nature of salt domes, and the number of attributes used for classification. The combination of edge based and texture based attributes ensures that the proposed algorithm works well even if the salt boundary is represented only by a weak reflector. We tested the proposed algorithm on the Netherlands offshore F3 block. Our experimental results show that the proposed algorithm can detect salt boundaries with high accuracy superior to existing gradient based as well as texture based techniques when used separately.