Adaptation of seismic skeletonization for other geoscience applications

Abstract

S U M M A R Y2-D grid skeletonization, modelled after seismic skeletonization, can be applied to any gridded data, including images commonly used in the geosciences. It affords a map of distinctly separable discontinuities together with a suite of attributes associated with each discontinuity. Such a map, with an accompanying catalogue of attributes, offers a broad scope for data analysis such as directional or any attribute-oriented filtering. The versatility of our method lies in the choice of the primitive feature set used for pattern recognition, and in the two-pass application of the detection process. Application of the method to different data sets, including images of drainage basins, potential field data, reflection seismic data and invertebrates provide examples of how attribute catalogues are analysed to extract additional data-dependent information such as directional, event length, pulse width and non-linearity characteristics.

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

“…After this step, the use of geometric or structural attributes based on the determination of reflector geometry, dip and trace coherence, similarity, variance, and curvature (Bahorich and Farmer, 1995;Gersztenkorn and Marfurt, 1999;Marfurt et al, 1999;Randen et al, 2000;Roberts, 2001;Al-Dossary and Marfurt, 2006;Marfurt, 2006;Chopra and Marfurt, 2008;Dewett and Henza, 2016) might favor the identification and enhancement of the amplitude and phase of seismic events (Acuña-Uribe et al, 2021 and references therein). It must also be noted that during the last few years, the detection of fault structures has been optimized using various techniques (Meldahl et al, 2001;Pedersen et al, 2002;Pepper and Bejarano, 2005;Vasudevan et al, 2005;Aqwari and Boe, 2011;Aqwari et al, 2012;Babangida et al, 2013;Hale, 2013;Di and Gao, 2017;Qi and Marfurt, 2018). On unconditioned legacy profiles, the main faults and related splays can be masked or complicated by several factors, including random noise, dispersion effects, and geological complexities due to intense deformation, which, in unmigrated data, might generate many diffractions.…”

Statistical analysis of spatial point patterns on deep seismic reflection data: a preliminary test

“…1997; Li et al . 1997; Rea & Knight 1998; Vasudevan & Cook 1998; Hurich & Kocurko 2000; Eaton & Vasudevan 2004; Vasudevan et al . 2005).…”

“…reflectors, and coherency-filtering (Neidell & Taner 1971;Milkereit & Spencer 1989;van der Baan & Paul 2000) and seismic skeletonization enhance the signal to noise ratio of the data. Such binary images have been analysed using classical statistics tools with a view to understand the geometrical patterns present in the data (Hurich 1996;Cook et al 1997;Li et al 1997;Rea & Knight 1998;Vasudevan & Cook 1998;Hurich & Kocurko 2000;Eaton & Vasudevan 2004;Vasudevan et al 2005). The processed binary images of the reflection seismic data are analogous to the processed binary images of the keratin filaments in that both are comprised of short-and long-linear segments, albeit the former has short-and long-curvilinear segments.…”

Enhancement of 3D Seismic Images using Image Fusion Techniques

“…In the first experiment: the number of inputs M is 2; one input is a seismic image (I R ) and the second is a seismic attribute named skeleton created by a skeletonization algorithm denoted as (I A ) [26]. The size of I R and I A is (876,221,271).…”