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GCAcquisition Footprint Removal for Better Fault and Curvature Attributes*
Satinder Chopra1, Kurt J. Marfurt2, and Somanath Misra1
Search and Discovery Article #40719 (2011)
Posted March 18, 2011
*Adapted from the Geophysical Corner column, prepared by the authors, in AAPG Explorer, March, 2011. Editor of Geophysical Corner is Bob A. Hardage ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic; Larry Nation is Communications Director.
1Arcis Corp., Calgary, Canada ([email protected])
2University of Oklahoma, Norman, Oklahoma
Seismic
attributes are particularly effective for extracting subtle geologic features from relatively noise-free
seismic
data. However,
seismic
data are usually contaminated by both random and coherent noise, even when the data have been migrated reasonably well and are multiple-free. As you can see here, certain types of noise can be minimized during interpretation through careful structure-oriented filtering and post-migration suppression of data-acquisition footprints.
Another problem sometimes encountered by interpreters is the relatively low frequency bandwidth of seismic
data. Although significant efforts are made during data processing to enhance frequency content of reflection signals, such efforts often fall short of the objective. Thus suitable ways need to be adopted to achieve improved frequency content of reflection data during data interpretation. We discuss both of these problems here – the suppression of acquisition footprints from
seismic
data, and frequency enhancement of data before final interpretation is done.
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Suppression of Random Noise Mean filters and median filters are commonly used during interpretation to suppress random noise. One valuable application is the use of dip-steered mean or median filters, which enhance laterally continuous events by reducing random noise yet does not suppress details consistent with geologic structure. Such a filter spans a defined number of data samples (N) within an aperture that follows local dip and azimuth and replaces the amplitude of the central sample with the median value of all N amplitudes spanned by the filter. Median filters can be applied iteratively, reducing random noise at each successive iteration, but they do not significantly increase the high-frequency geological components of the surface along which they operate. Dip-steered mean filters work well on prestack data in which discontinuities appear as smooth diffractions, but they tend to smear faults and stratigraphic edges on migrated data. Dip-steered median mean filters work somewhat better, but they too can smear faults. Structure-oriented filters operate parallel to reflectors and do no filtering or smoothing perpendicular to a reflector. Suppression of Acquisition Footprint An acquisition footprint is defined as any amplitude or phase anomaly observed in One of the simplest methods for suppressing data-acquisition footprints is to apply kx-ky filters on Thin-bed spectral inversion is a process that removes time-variant wavelets from In addition to viewing spectrally broadened Depending on the quality of data being interpreted, as well as access to the methods discussed here, data need to be preconditioned to optimize noise removal (whether the noise removal involves random noise or unwanted acquisition footprints) and to achieve optimal frequency-enhancement before attributes are computed. Once such data preconditioning is done, To illustrate the importance of data preconditioning, Figure 2 shows stratal slices from coherence volumes run on (a) input data, (b) input data with inverse Q filtering, (c) spectrally whitened input data, and (d) input data transformed to filtered thin-bed reflectivity inversion. Notice these coherence slices show increased resolution in this a-b-c-d order of data preconditioning, with the highest lateral resolution seen for coherence computed from filtered thin-bed reflectivity inversion. We emphasize that computation of attributes is not a process that involves pressing some buttons on a workstation, but requires careful examination of input In our studies, we find that:
Some of these data-conditioning methods may not be available to an interpreter; we hope these examples assist in decisions about how We wish to thank Arcis Corporation for permission to present these results. |
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