Structure oriented filter seismic rokdoc

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GPYSA7 0016-8033 Geophysics Abstract Web of Science Google Scholar J., 1999, EigenstructureвЂђbased coherence computation as an aid to 3вЂђD structural and stratigraphic mapping: Geophysics, 64, 1468вЂ“1479.

Bahorich, M., and Farmer, S., 1995, The coherence cube: The Leading Edge, 14, 1053вЂ“1058. Examples show the remarkable effects of this operation. We discuss one particular implementation of this principle in some detail: a simulated anisotropic diffusion process (lowвЂђpass filter) that diffuses the seismic amplitude while the diffusion tensor is computed from the local image structure (so that the diffusion is parallel to the reflections). These filters therefore have three ingredients: (1) orientation analysis, (2) edge detection, and (3) edgeвЂђpreserving oriented smoothing. The gist of these filters is a smoothing operation parallel to the seismic reflections that does not operate beyond reflection terminations (faults). We discuss a class of filters that removes noise and, if desired, simplifies structural information in 3D seismic data. If the interpreters so wish, they can refine such an initial crude structural interpretation in selected target areas. Without noise and smallвЂђscale structure, both man and machine (autotrackers) can arrive at a structural interpretation faster. The essential element is the stepwise removal of noise, and eventually of smallвЂђscale stratigraphic and structural features, to derive more and more simple representations of structural shape. The structure-oriented space-varying median filter can also be easily embedded into an iterative deblending procedure based on the shaping regularization framework and can help obtain much improved deblending performance.We present a new approach to structural interpretation of 3D seismic data with the objectives of simplifying the task and reducing the interpretation time. Synthetic and real data examples show that structure-oriented space-varying median filter can significantly improve the signal preserving performance for curving events in the seismic data. We apply the structure-oriented space-varying median filter method to remove the spike-like blending noise arising from the simultaneous source acquisition. We show the key difference among different types of median filters in detail and demonstrate the principle of the structure-oriented space-varying median filter method. A structure-oriented space-varying median filter can adaptively squeeze and stretch the window length of the median filter when applied in the locally flattened dimension of an input seismic data in order to deal with the dipping events caused by inaccurate slope estimation. We propose a type of structure-oriented median filter that can effectively attenuate spike-like noise even when the local slope is not accurately estimated, which we call structure-oriented space-varying median filter. When local slope contains significant error, which is usually the case for noisy data, the structure-oriented median filter will still cause severe damages to useful energy. The performance of a structure-oriented median filter highly depends on the accuracy of the estimated local slope from the noisy data. In seismic data processing, the median filter is usually applied along the structural direction of seismic data in order to attenuate erratic or spike-like noise. A new paper is added to the collection of reproducible documents: Deblending of simultaneous-source data using a structure-oriented space-varying median filter

Structure oriented filter seismic rokdoc