Fault picking is a critical, but human-labor-intensive component of seismic interpretation. In a bid to improve fault imaging in seismic data, we have applied a directional Laplacian of a Gaussian operator to sharpen fault features within a coherence volume. We computed an M × M matrix of the second moment tensor distanceweighted coherence values that fell within a 3D analysis window about each voxel. The eigenvectors of this matrix defined the orientation of planar discontinuities, whereas the corresponding eigenvalues determined whether these discontinuities were significant. The eigenvectors, which quantified the fault dip magnitude and dip azimuth, defined a natural coordinate system for smoothing of the planar discontinuity. We rotated the data to the new coordinate system and applied the sharpening operator. By comparing the vector dip of the discontinuity to the vector dip of the reflectors, we could apply a filter to either suppress or enhance discontinuities associated with unconformities or low-signal-to-noise-ratio shale-on-shale reflectors. We have revealed the value and robustness of the technique by application to two 3D data volumes from offshore New Zealand, which exhibited polygonal faulting, shale dewatering, and mass transport complexes.

Along with horizon picking, fault identification and interpretation is one of the key components for successful seismic data interpretation. Significant effort has been invested in accelerating seismic fault interpretation over the past three decades. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using coherence or other edge-detection attributes, which in turn serve as inputs for automatic fault extraction using image processing or machine learning techniques. Because seismic data exhibit not only structural reflectors but also seismic noise, we have developed a fault attribute workflow that contains footprint suppression, structure-oriented filtering, attribute computation, “unconformity” suppression, and our new iterative energy-weighted directional Laplacian of a Gaussian (LoG) operator. In general, tracking faults that exhibit a finite offset through a suite of conformal reflectors is relatively easy. Instead, we evaluate the effectiveness of this workflow by tracking faults through an incoherent mass-transport deposit, where the low-frequency contribution of multispectral coherence provides a good fault image. Multispectral coherence also reduces the “stair-step” fault artifacts seen on broadband data. Application of statistical filtering can preserve the discontinuity’s boundaries and reject incoherent backgrounds. Finally, iterative application of an energy-weighted directional LoG operator provides improved fault image by sharpening low-coherence anomalies perpendicular and smoothing low-coherence anomalies parallel to fault surfaces, while at the same time attenuating locally nonplanar anomalies.

Acquisition footprint manifests itself on 3D seismic data as linear grid pattern of noise on time or horizon slices. Ideally, footprint suppression should be handled in the processing workshop. Unfortunately this is not an option for vintage poststack volumes where no pre-stack data exist. In this work we explore the use of a modified Continuous Wavelet Transform in a bid to suppress the footprint. The method involves decomposing the time slices of the seismic data and attributes into voices and magnitudes using raised-cosine filters. We rely on seismic attribute ability to highlight acquisition footprint to design a mask to suppress it.