Beamlet transform has obvious effect in extraction of linear and curvilinear features. The seismic coherence slice can be regarded as an image in three-dimensional seismic interpretation. Beamlet transform can identify and extract linear and curvilinear features in the slice. This paper describes the basic principles of the beamlet transform as well as its advantage in linear feature extraction of the noise-containing image. The authors used the beamlet transform to guide the fault interpretation of seismic slice data obtained in Yuanba area of Sichuan. The results show that the method has obvious advantages in interpretation of small faults and fissures.
[1] Donoho D L,Huo Xiaoming.Beamlets and multiscale image analysis[C]//Multiscale and Multiresolution Methods.Berlin:Springer Press,2001:149 -196.[2] Anthony F.Large-Scale structures in the universe[M].West Sussex:Chichester University,1998.[3] Halina A,David H,Ury N,et al.An Orientation Selective Neural Network and its Application to Cosmic Muon Identification[J].Nucl. Instr. Meth. Phys. Res.,1996,378:305-311.[4] David L D.Wedgelets:nearly minimax estimation of edges[J].Annals of Statistics,1999,27(3):859~897.[5] Huo Xiaoming.Sparse Image Representation via Combine Transforms.Stanford:Stanford University,1999.[6] 梅小明,张良培,李平湘.利用Beamlet变换算法提取遥感图像线性特征[J].计算机应用研究, 2008, 25(5):1576-1578.[7] 杨明,尹勇,彭玉华,等.Beamlet变换与多尺度线特征提取算法[J].电子学报,2007,35(1):100-103.
