Image processing of seismic attributes for automatic fault extraction

Abstract

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.