2.5D multifocusing imaging of crooked-line seismic surveys

Abstract

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

High‐resolution 2.5D multifocusing imaging of a crooked seismic profile in a crystalline rock environment: Results from the Larder Lake area, Ontario, Canada

“…The parameter $$\Delta d_S$$ (or $$\Delta d_G$$) is the projected offset between source (or receiver) and $$M_{0}^{\prime }$$ along the azimuth of the true dip. The point $$M_{0}^{\prime } = [X_{M_{0}^{\prime }}, Y_{M_{0}^{\prime }}]$$ is the intersection between an orthogonal line to the traverse line at the image point $$[X_0, Y_0]$$ and a line that connects the source and receiver (see Appendix A in Jodeiri Akbari Fam et al., 2021a).…”

“…The 2.5D MF method corrects normal, inline and crossline dip moveouts simultaneously, which leads to more accurate time shifts and alignment of reflections (Jodeiri Akbari Fam et al., 2021a). This approach preserves the azimuth and accounts for the spatial distribution of the sources and receivers to address midpoint dispersal problems.…”

“…All these factors make the imaging of crooked‐line seismic data from hard‐rock settings challenging compared to data from sedimentary basins (Malehmir et al., 2012; Milkereit et al., 2000). Application of standard 2D processing methods often results in poor images in these circumstances (see Jodeiri Akbari Fam et al., 2021a; Nedimović & West, 2003a) since they are unable to approximate wavefields accurately and use relatively low‐fold data and simplified velocity fields that limit the signal‐to‐noise ratio improvement (e.g., Bécel et al., 2015).…”

“…Therefore, those early approaches cannot be applied to crooked‐line seismic configurations. For this reason, we developed an algorithm that takes into account the cross‐dip effects, source–receiver azimuth and coordinates variation in 3D space, called 2.5D MF (Jodeiri Akbari Fam et al., 2021a).…”

“…The MF methods are capable of generating high‐resolution stack sections in this type of noisy environment and are better suited to model the moveout of diffracted waves (Belfer et al., 2008; Berkovitch et al., 2004, 2008, 2011, 2012; Borghi et al., 2017; Chang, Zhang, et al., 2019; Curia et al., 2017; Gurevich et al., 2002; Landa et al., 1999). However, the early implementations of MF methods did not consider cross‐dip effects and horizontal variation of source and receiver coordinates (or variations in the azimuth of each source–receiver pair) in 3D space (Jodeiri Akbari Fam et al., 2021a). Therefore, those early approaches cannot be applied to crooked‐line seismic configurations.…”