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GCAdding Texture Attributes
to the 3-D Mix*
Paul de Groot1, Farrukh Qayyum1, and Nanne Hemstra1
Search and Discovery Article #41244 (2013)
Posted November 25, 2013
*Adapted from the Geophysical Corner column, prepared by the author, in AAPG Explorer, November, 2013.
Editor of Geophysical Corner is Satinder Chopra ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic
1dGB Earth Sciences, Enschede, Netherlands ([email protected])
In a previous Geophysical Corner “A New Approach to Stratigraphic Interpretation”, Search and Discovery Article #41195, (http://www.searchanddiscovery.com/documents/2013/41195qayyum/qayyum.htm?q=+textStrip:41195) we introduced a new set of seismic
attributes
that play an important role in extracting detailed stratigraphic information from
seismic
data. The
attributes
in question were derived from a HorizonCube, an interpretation technique that provides fully interpreted
seismic
volumes where horizons are automatically tracked between a given set of framework horizons and faults. Here, we go further and examine one other set of
attributes
– specifically, texture
attributes
, and how they can combine with HorizonCube
attributes
for 3-D segmentation.
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Neural network-based waveform segmentation workflows have proved to be a highly valuable instrument for Neural network-based segmentation also can be performed in 3-D. However, this cannot be done by feeding the neural network with waveforms, as is done in the horizon-guided approach. This is because waveforms change along the application window while segment centers are fixed.
The solution to this problem is to feed the neural work with phase-independent
There are two particular sets of ![]() ![]() ![]() ![]() Texture A GLCM is a 2-D matrix of N x N dimensions representing the amplitude values of the reference pixel versus the amplitudes of the neighboring pixel. The matrix is filled by comparing each amplitude in the input area (volume) with its direct neighbor and increasing the occurrence of the corresponding matrix cell. This is repeated for all amplitude pairs in the input cube, which then are converted into probabilities. The GLCM thus captures how probable it is to find pairs of neighboring amplitudes in the area (volume) around the evaluation point. Texture The GLCM input volume can be “dipsteered,” meaning that the input follows the stratigraphic layering, which results in sharper attribute responses for dipping strata. Three groups of texture In each group, the Figure 1 shows a horizon-guided UVQ waveform segmentation map that captured the The NW-SE oriented dark brown-red features on the right are sand ridges of 10-20 meters in height, developed parallel to the coast. These features are analogous to present day deposits observed along the Dutch coast. Furthermore, NE-SW oriented deepwater channel systems are recognized (purple-red, on the left). These narrow channel-levee systems are developed as a result of halo kinetic movement of Zechstein Salt in the northeast (upper right) corner of the image. Up-dip these channels cross-cut the sand ridges while down-dip they meander and bifurcate into the basin, where turbiditic deposits could be developed.
Figure 2 shows a
In addition to texture and energy HorizonCube |