In today’s industry, automatic detection of geologic features such as faults and channels is a challenging problem when the quality of data is not good. Edge detection filters are generally applied for the purpose of locating such features. Until now, edge detection has been carried out on rectangularly sampled 3D seismic data. The computational cost of edge detection can be reduced by exploring other sampling approaches instead of the regular rectangular sampling commonly used. Hexagonal sampling is an alternative to rectangular sampling that requires 13.4% less samples for the same level of accuracy. The hexagonal approach is an efficient method of sampling with greater symmetry compared with the rectangular approach. Spiral architecture can be used to handle the hexagonally sampled seismic data. Spiral architecture is an attractive scheme for handling 2D images that enables processing 2D data as 1D data in addition to the inherent hexagonal sampling advantages. Thus, the savings in number of samples, greater symmetry, and efficient data handling capability makes hexagonal sampling an ideal choice for computationally exhaustive operations. For the first time to our knowledge, we have made an attempt to detect edges in hexagonally sampled seismic data using spiral architecture. We compared edge detection on rectangular and hexagonally sampled seismic data using 2D and 3D filters in rectangular and hexagonal domains. We determined that hexagonal processing results in exceptional computational savings, when compared with its rectangular processing counterpart.
