Enhancement of diffractions in prestack domain by means of a finite-offset double-square-root traveltime

Abstract

Exploration of redundancy contained in the seismic data set assures enhancement of images that are based on stacking results. This enhancement is the goal of developing multiparametric traveltime equations that are able to approximate reflection and diffraction events in general source-receiver configurations. The main challenge of using these equations is to estimate a large number of parameters in a computationally feasible, reliable, and fast way. To obtain a better fit for diffraction traveltime events than the ones in the literature, we have derived a finite-offset (FO) double-square-root (DSR) diffraction traveltime equation (which depends on 10 parameters in three dimensions and four parameters in two dimensions). Moreover, to reduce the number of parameters, we have developed another version called simplified FO-DSR diffraction traveltime equation (which depends on five parameters in three dimensions and two parameters in two dimensions), which delivers a similar performance. We have developed operators that make use of the simplified FO-DSR traveltime equation to construct the so-called diffraction-only data set volumes (or, more simply, D-volumes) assuring enhancement in the diffraction extraction process. The D-volume construction has two steps: first, a stacking procedure to separate the diffraction events from the input data set and second, a spreading procedure to enhance the quality of these diffractions. As proof of concept, our approach has been tested on 2D/3D synthetic and 2D field data sets with successful results.

Quantifying anisotropy parameters in weakly anisotropic media through diffraction traveltime parameter clusters

“…Considering no prior knowledge about the subsurface geological structures, the challenge is to make the extraction of kinematic parameters with precision from the seismic data. For example, the tools given by Faccipieri et al (2016) and Coimbra et al (2019) can perform such parameter extraction. The traveltime approximation that describes the kinematics behind these cited operators can be described as…”

“…Therefore, determining the kinematic contributions for diffraction response is a topic of great interest, with applications varying from structural characterization to velocity analysis. Several authors have studied the use of diffractions in seismic processing (e.g., Landa and Keydar, 1998;Dell et al, 2013;Waheed et al, 2013;Gelius and Tygel, 2015;Faccipieri et al, 2016;Waheed et al, 2017;Coimbra et al, 2019).…”

Diffraction pattern recognition using deep semantic segmentation

“…et al ., 2016). Good examples are nonetheless available on synthetic data and from sedimentary basins (e.g., Fomel et al ., 2007; Dell and Gajewski, 2011; Klokov and Fomel, 2012; Coimbra et al ., 2018; Coellho et al ., 2019; Kim et al ., 2020; Wang et al ., 2020; Li et al ., 2021; Sheng and Zhao, 2022).…”

Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging

“…Further, these diffraction data are also used for travel time approximation for anisotropic imaging [3]. The separation of diffraction is widely used [4][5][6][7] for enhancement of seismic imaging, such as fault, fracture, complex variable velocity structure, and karstification imaging [8]. Further, the diffraction response for a nonzero separation of the source and receiver was presented by Berryhill in 1977 [9], in which theoretical support was obtained for applying the zero-separation theory to stack the seismic data.…”