Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Abstract

A B S T R A C TPrestack depth imaging of seismic data in complex areas such as salt structures requires extensive velocity model updating. In many cases, salt boundaries can be difficult to identify due to lack of seismic reflectivity. Traditional amplitude based segmentation methods do not properly tackle this problem, resulting in extensive manual editing. This paper presents a selection of seismic attributes that can reveal texture differences between the salt diapirs and the surrounding geology as opposed to amplitude-sensitive attributes that are used in case of well defined boundaries. The approach consists of first extracting selected texture attributes, then using these attributes to train a classifier to estimate the probability that each pixel in the data set belongs to one of the following classes: near-horizontal layering, highly-dipping areas and the inside of the salt that appears more like a low amplitude area with small variations in texture. To find the border between the inside of the salt and the highly-dipping surroundings, the posterior probability of the class salt is input to a graph-cut algorithm that produces a smooth, continuous border. An in-line seismic section and a timeslice from a 3D North Sea data set were employed to test the proposed approach. Comparisons between the automatically segmented salt contours and the corresponding contours as provided by an experienced interpreter showed a high degree of similarity.

A genetic algorithm for filter design to enhance features in seismic images

“…Considering these problems, several methods for automating and improving the visualization process to facilitate interpreter's tasks have been proposed (e.g., Gelchinsky and Shtivelman ; Dumay and Fournier ; Dopkin, James and Neri ; AlBinHassan, Luo and Al‐Faraj ; Berthelot et al . ; Orozco‐del‐Castillo et al . ).…”

“…It is also highly subjective: different interpreters may have contrasting interpretations. Considering these problems, several methods for automating and improving the visualization process to facilitate interpreter's tasks have been proposed (e.g., Gelchinsky and Shtivelman 1983;Dumay and Fournier 1988;Dopkin, James and Neri 2003;AlBinHassan, Luo and Al-Faraj 2006;Berthelot et al 2011;Orozco-del-Castillo et al 2011b). Jeong, Whitaker and Dobin (2006) developed a seismic fault detection system -using non-linear filters and a graphics processing unit implementation -which made interactive fault detection feasible.…”

“…Seismic attributes can enhance certain characteristics of interest in seismic data (Bahorich and Farmer 1995;Marfurt et al 1999). Berthelot et al (2011) presented a semi-automatic methodology for detection of salt diapirs without defined boundaries based on the concept that seismic attributes can reveal texture differences between the diapirs and the surrounding geology. By selecting different texture attributes and using them to train a classifier, they were able to estimate the probability that each pixel in the data set belonged to some predetermined classes.…”