Improving fault images using a directional Laplacian of a Gaussian operator

Machado Acosta, Gabriel

Improving fault images using a directional Laplacian of a Gaussian operator

dc.contributor.advisor	Marfurt, Kurt
dc.contributor.author	Machado Acosta, Gabriel
dc.contributor.committeeMember	Slatt, Roger
dc.contributor.committeeMember	Pigott, John
dc.date.accessioned	2016-05-11T16:47:04Z
dc.date.available	2016-05-11T16:47:04Z
dc.date.issued	2016-05
dc.date.manuscript	2016-05-10
dc.description.abstract	Fault picking is a critical, but human-intensive component of seismic interpretation. In a bid to improve fault imaging in seismic data, I have applied a directional Laplacian of a Gaussian (dLoG) operator to sharpen fault features within a coherence volume. I compute an M by M matrix of the second moment distance-weighted coherence tensor values that fall within a 3D spherical analysis window about each voxel. The eigenvectors of this matrix define the orientation of planar discontinuities while the corresponding eigenvalues determine whether these discontinuities are significant. The eigenvectors, which quantify the fault dip-magnitude and dip-azimuth, define a natural coordinate system for both smoothing and sharpening the planar discontinuity. By comparing the vector dip of the discontinuity to the vector dip of the reflectors, I can apply a filter to either suppress or enhance discontinuities associated with unconformities or low signal-to-noise ratio shale-on-shale reflectors. Such suppression become useful in the implementation of subsequent skeletonization algorithms. Automatic fault picking processes for accelerated interpretation of basins also become much easier to implement and more accurate. I demonstrate the value and robustness of the technique through application to two 3D post stack data volumes from offshore New Zealand, which exhibit polygonal faulting, shale dewatering, and mass-transport complexes. Finally, I use these filtered faults as input to an ant-tracking algorithm and automatic fault extraction and find significant improvement in the speed and accuracy of fault interpretation.	en_US
dc.identifier.uri	http://hdl.handle.net/11244/34610
dc.language	en_US	en_US
dc.subject	Operator	en_US
dc.subject	Laplacian of a Gaussian	en_US
dc.subject	Fault enhancement	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Improving fault images using a directional Laplacian of a Gaussian operator	en_US
ou.group	Mewbourne College of Earth and Energy::Conoco Phillips School of Geology and Geophysics	en_US