Semblance, coherence, and other discontinuity attributes

Abstract

<jats:p> Discontinuity calculations, also called coherence or semblance, are some of the most commonly used seismic attributes. They measure the amount of similarity between adjacent seismic traces. However, there are several types of discontinuity estimates and a plethora of names for similar attributes. As a result, it can be difficult to understand the differences among them. Although the discontinuity algorithm that any particular software package uses might be unknown, most are based on one of a few published methods. Understanding published discontinuity attributes gives insight into the trade-offs among different proprietary implementations. Therefore, in this tutorial and the accompanying Jupyter notebook, we will explore simplified Python implementations of a few widely used discontinuity algorithms. </jats:p>

Real-time seismic-image interpretation via deconvolutional neural network

“…homogeneity and low contrast in GLCM maps (Gao, 2003), but isolating it from the surrounding patterns requires additional tools used in computer graphic and imaging processing. For example, normalized cuts (Lomask et al, 2007) detects salt domes by solving a global optimization problem. The active-contour-models method (Shafiq et al, 2015) starts with the initial boundary from interpreters and then gradually deform it to fit the salt boundary observed in the attribute image.…”

“…While clearly highlighting the major faults of apparent displacements, however, most of the edge-detection tools are less efficient for subtle structure interpretation, such as fracture characterization and facies analysis, in which the lateral variation of seismic signals is subtle and beyond the resolution of edge detectors. Detailed summaries of the edge detection can be found in Chopra (2002), Kington (2015), and Di and Gao (2017a). For the purpose of detecting the small-scale structures, such as subtle faults and fractures, geophysicists turn to evaluating the variation of the geometry of seismic reflectors, which successfully link the fractures with the high-order reflector geometric attributes, such as curvature (Roberts, 2001) and flexure (Gao, 2013).…”

Semi‐automatic fault/fracture interpretation based on seismic geometry analysis

“…() performed volumetric fault imaging by applying the directional Laplacian of a Gaussian filter to coherence anomalies along reflector dip and azimuth. A comprehensive summary of the discontinuity analysis can be found in Chopra (), Kington () and Di and Gao ().…”

Patch-level MLP classification for improved fault detection

“…Since then, substantial efforts have been devoted for improving such attribute as well as its variations in terms of detection resolution and noise robustness (e.g., Luo et al, 1996;Marfurt et al, 1998;Gersztenkorn and Marfurt, 1999;van Bemmel and Pepper, 2000;Cohen and Coifman, 2002;Tingdahl and de Rooij, 2005;Gao, 2014a, Wang et al, 2016). A comprehensive summary of the edge-detection attributes can be found in Chopra (2002) and Kington (2015). However, the conventional seismic edgedetection is limited in its detection resolution for small-scale faults and fractures beyond the seismic scale and moreover offers no physical link for predicting the fundamental fracture properties (e.g., intensity, orientation, and sense of displacement) either quantitatively or qualitatively (Gao, 2013).…”