A novel approach for salt dome detection using a dictionary-based classifier

In this paper, we present a dictionary based classification approach for salt dome detection using texture based 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 algorithm works by combining the attributes from the Gray Level Co-occurrence Matrix (GLCM) and those from the Gradient of Texture (GoT) attributes with a dictionary-based learning approach to classify the boundaries of salt regions. The combination of GLCM and GoT attributes ensures that the proposed algorithm works well even if the salt boundary is represented only by a weak reflector. Contrary to other texture attributes based salt dome detection techniques, our algorithm works with a minimum set of features and is shown to be independent of the amplitude variations in seismic data. We tested the proposed algorithm on the Netherlands offshore F3 block. Our experimental results show that the proposed algorithm can detect salt bodies with high accuracy superior to existing gradient based as well as texture based techniques when used separately.