Name
Abstract | ||
|---|---|---|
This paper presents a structure-oriented Gaussian (SOG) filter for reducing noise in 3D reflection seismic data while preserving relevant details such as structural and stratigraphic discontinuities and lateral heterogeneity. The Gaussian kernel is anisotropically constructed based on two confidence measures, both of which take into account the regularity of the local seismic structures. So that, the filter shape is well adjusted according to different local geological features. Then, the anisotropic Gaussian is steered by local orientations of the geological features (layers) provided by the Gradient Structure Tensor. The potential of our approach is presented through a comparative experiment with seismic fault preserving diffusion (SFPD) filter on synthetic blocks and an application to real 3D seismic data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413869 | ICIP |
Keywords | Field | DocType |
confidence measures,structure oriented gaussian filter,signal denoising,noise reducing,local geological features,anisotropic gaussian,confidence measure,filter shape,gaussian kernel,anisotropic gaussian filter,smoothing methods,seismology,seismic data,structure-oriented gaussian,lateral heterogeneity,sfpd filter,seismic detail preserving smoothing,local orientation,3d reflection seismic data,reflection seismic data,sog filter,seismic detail,structure-oriented gaussian filter,geophysical signal processing,gradient structure tensor,filters,structure-oriented,seismic fault,local seismic structure regularity,stratigraphic discontinuities,different local geological feature,seismic fault preserving diffusion filter,local seismic structure,structural discontinuities,noise,data structure,coherence,tensile stress,kernel,filtering | Computer science,Artificial intelligence,Gaussian function,Gaussian filter,Kernel (linear algebra),Mathematical optimization,Classification of discontinuities,Pattern recognition,Algorithm,Filter (signal processing),Smoothing,Gaussian,Structure tensor | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |