Salt-Dome Detection Using a Codebook-Based Learning Model
In this letter, we present a novel supervised codebook-based learning model for salt-dome detection in seismic imaging using texture-based attributes. The proposed algorithm is data driven and overcomes the limitations 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 cooccurrence matrix (GLCM) and those from the Gabor filter, with a codebook-based learning approach to delineate salt boundaries in seismic data. The combination of GLCM- and Gabor-filter-based attributes ensures that the algorithm works well even in the absence of strong reflectors along the salt boundary. Contrary to existing salt-dome detection techniques, our algorithm works with a codebook of small size and is shown to be robust and computationally efficient. The learning properties of the codebook-based model make the algorithm flexible and adaptable to the nature of time-scale varying data acquired in seismic surveys. We used the Netherlands F3 block to evaluate the performance of the proposed algorithm. Our experimental results show that the proposed codebook-based workflow can detect salt domes with good accuracy, superior to existing salt-dome detection techniques.