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

Abstract

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