Operator‐oriented CRS interpolation

Abstract

A B S T R A C TIn common-reflection-surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. Using the common-reflection-surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common-reflection-surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common-reflection-surface-based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common-receiver volume and binned common-offset subsets of a 3D marine data set. The impact of a common-reflectionsurface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO-interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.

Common‐offset common‐reflection‐surface attributes estimation with differential evolution

“…Another benefit of the CRS operator is the seismic data regularization (Hoecht et al . ; Xie and Gajewski ) by interpolating seismic traces to positions with missing or null traces. This benefit is also observed and extended to the pre‐stack data in the CO CRS method.…”

Seismic image enhancement in post stack depth migration by finite offset CDS stack method

“…These contributions are tested by coherence analysis for each ZO sample, and the set of attributes, which yield the highest coherency, is accepted as the optimum operator parameters to perform the actual stack (Höcht et al 2009). Figure 2c, shows the green CRS stack operator compared to the conventional CMP stack in Fig.…”

3D Common Offset CRS for Data Pre-conditioning

“…The same technique was applied by Höcht et al (2009) for the interpolation in the CS and CMP domain. Müller et al (2010) showed the potential of the CO-CRS method for regularization and S/N improvement of complete pre-stack datasets, as well as the superiority of the method with respect to the ZO-CRS-based data enhancement for data containing non-hyperbolic move-out.…”