Structure oriented filter seismic rokdoc

We discuss one particular implementation of this principle in some detail: a simulated anisotropic diffusion process (low-pass 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) edge-preserving 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 small-scale 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 small-scale stratigraphic and structural features, to derive more and more simple representations of structural shape. Geostatistics allows merging several data sets (different acquisition techniques or vintages in 4D seismic framework) in a single coherent velocity cube using multivariate kriging techniques.We present a new approach to structural interpretation of 3D seismic data with the objectives of simplifying the task and reducing the interpretation time. 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. The calibration of velocity functions and models takes into account wells and seismic data as well as geological features and velocity trends. The seismic provides a shape function with stationary residuals on top of it. Geostatistical tools deal with non-stationary features such as vertical trends. The geostatistical seismic data enhancement workflow has several advantages: Geostatistical filtering result вЂ“ (1) before processing вЂ“ (2) after processing Geostatistical filteringВ allows the removal of acquisition footprints, patterns due to oriented picking, white or coherent noise on velocities or amplitudes.ДЇiltering and quality control are possible on 2D/3D gridded data but also on scattered data such as seismic picks. Geostatistics characterizes the intrinsic structure of the parameter in order to take into account the continuity of your data and introduce the geology by capturing the geological structures. Geostatistics allows analyzing seismic data through a probabilistic approach based on a spatial variability analysis. It gives quantitative and qualitative information on major spatial data structures.ДЇILTERING BASED ON THE CHARACTERIZATION OF SPATIAL CONTINUITY Experimental variogram is an essential tool for the structural interpretation of the data. Geological effects can also be highlighted like salt bodies, strong slopes and important thickness differences. The principal benefit is to emphasize inconsistencies such as seismic folds during acquisition (around the wells). SETTING STATISTICAL EVIDENCE OF ANOMALIESДirectional statistics allow highlighting zones with special characteristics or the presence of test lines. Geostatistical tools allow a control of spatial coherence of events related to seismic velocities. Geostatistics enables quantifying the magnitude of artifacts and subsequently the quality of the processing. Geostatistics is particularly well suited for this task, allowing an independent review of the data quality that can be achieved at any step in your process. The geostatistical seismic data enhancement workflow is made of several steps:Д¬ontrolling data quality is compulsory in order to identify artifacts or anomalies. In comparison with standard geophysical filters such as Wiener, Median or F-k, factorial kriging (Matheron, 1982) allows removing artifacts that are spatially correlated. Filtering is frequently required in order to remove undesirable noise (acquisition footprint, patterns due to oriented processing windows, random noise) on various seismic attributes (e.g.

Structure oriented filter seismic rokdoc