Seismic velocity analysis in the presence of amplitude variations using local semblance

Abstract

Obtaining accurate velocity models plays a crucial role in many routine seismic imaging algorithms. Seismic velocity models are normally made through seismic velocity analysis workflows. The routine workflows are not capable of dealing with polarity variations across moveout curves. We address this limitation by proposing a straight‐forward and robust semblance‐based workflow, which is a modified version of the conventional semblance function. The coherency function applies semblance analysis on separate clusters of receivers followed by averaging the corresponding coherency measures from all the clusters. The proposed approach is suitable for any case of amplitude variations including attenuation and any class of amplitude‐versus‐offset effects. The ability of the proposed workflow is demonstrated to two synthetic data as well as two field‐recorded common‐midpoint gathers. We perform accuracy analysis by comparing the results from the proposed approach with the results achieved from conventional velocity analysis, and another semblance‐based algorithm that is developed to address the polarity variation task. We also studied noise sensitivity analysis by computing and comparing mathematical expectations between theory and practice.

Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

“…The estimated average relative error for the presented field data example is approximately 28%, which is fairly acceptable for field data reconstruction. It is worth mentioning (Khoshnavaz et al, 2021), and (b) the corresponding extrapolated TWTs using Equation ( 8) that local slopes are always estimated relying on the dominant amplitude in each sample point. Therefore, either primaries or multiples are neglected in slope estimation and predictive painting.…”

“…(a) Velocity spectrum associated with Figure 9(b) computed by local semblance analysis (Khoshnavaz et al ., 2021), and (b) the corresponding extrapolated TWTs using Equation ()…”

“…I use the well‐known hyperbolic formulation (Equation ()) for velocity analysis, which is given by $$\begin{equation}{t^2} = {t_0}^2\ + {\left( {\frac{x}{v}} \right)^2},\end{equation}$$where t , t 0 , x and v denote TWT, vertical/zero‐offset TWT, offset and NMO velocity, respectively. Figure 14(a) shows the corresponding velocity spectrum computed for CMP gather of Figure 9(b) using local semblance (Khoshnavaz et al ., 2021) and the black line shows the NMO velocities picked by automatic picking. Given the NMO velocities, moveout curves are reconstructed in the whole CMP gather using Equation ().…”

Coherency analysis of polarity reversed diffracted wavefields using local semblance

“…To circumvent this task in the context of diffraction imaging, we employ the local semblance approach suggested by Khoshnavaz et al (2019Khoshnavaz et al ( , 2021 used to detect non-explosive passive seismic events and to perform velocity analysis in the case of class II amplitude-versus-offset anomalies. To do so, we split the scanning window into a smaller number of windows/groups, which include a smaller number of receivers in each group.…”

“…This approach was previously introduced by Khoshnavaz et al (2019) to deal with polarity reversals caused by microseismic source mechanisms. The approach was also employed by Khoshnavaz et al (2021) in seismic velocity analysis to fix the problem of polarity reversals caused by class II amplitude-versus-offset (AVO) anomalies in commonmidpoint (CMP) domain. Following their works, we propose to merge the popular time-domain diffraction imaging workflow of Landa et al (1987) and Landa and Keydar (1998) with local semblance analysis along two-way-time curves.…”