3-D Structure-Oriented Adaptive Gaussian Pyramid for Seismic Multiscale Fracture Detection

Gaussian pyramid (GP) has the property of decomposing the 2-D data into multiple scales. It has been successfully applied in image processing, but rarely used in seismic data processing and interpretation. Specifically, seismic data are not just a 2-D image in one time slice or horizon. Moreover, the pattern of geological body varies rapidly in 3-D spaces. Hence, the classical GP method exists a limitation in processing 3-D seismic data. Especially in fracture detection, the 2-D isotropic Gaussian kernel used in GP tends to blur the fracture details. In this letter, to match the high-dimensional seismic data, we propose a structure-oriented adaptive Gaussian pyramid (SOA-GP) algorithm, which expands the 2-D GP method to the 3-D situation. In this case, to eliminate the influence of transverse change of wave impedance and consider stratigraphic characteristics, the 2-D Gaussian filter is also substituted by the 3-D structure-adaptive anisotropic Gaussian kernel, which is constructed by instantaneous phase-based gradient structure tensor (GST) method. Meanwhile, seismic attributes are applied to the multiresolution seismic data decomposed by the SOA-GP method to realize multiscale fracture detection. Finally, we apply this new 3-D SOA-GP method to field data. It demonstrates that the multiscale decomposition of the new method could produce more details of fracture and less disturbance from noise.