Combination of the CRS-based pre-stack data regularization and RTM: application to real land data

The application of the reverse time migration (RTM) in land seismic data is still a great challenge due to its low quality, low signal-to-noise ratio, irregular spatial sampling, acquisition gaps, missing traces, etc. Therefore, prior to the application of this kind of depth migration, the input pre-stack data must be conveniently preconditioned, that is, it must be interpolated, regularized, and enhanced. There are several methods for seismic data preconditioning, but for 2D real land data, the regularization of pre-stack data based on common reflection surface (CRS) stack method provides high quality enhanced preconditioned data, which is suitable for pre-stack depth migration and velocity model building. This work demonstrates the potential of RTM combined with CRS-based pre-stack data regularization, applied to real land seismic data with low quality and irregularly sparse spatial sampled, from geologically complex areas with the presence of diabase sills and steep dip reflections. Usually, determining the wavelet of the seismic source from land data is a challenge, because of this, RTM migration is often applied using artificial sources (e.g. Ricker). In this work, from the power spectrum of the pre-stacked data, we determine the wavelet of the seismic source to apply the RTM to real land data. We present applications of the pre-stack data preconditioning based on CRS stack and of the RTM in 2D land data of Tacutu and Parnaiba Basins, Brazil. Comparisons with the standard Kirchhoff depth migration reveals that the RTM improves the quality and resolution of the migrated images.

Off-the-grid vertical seismic profile data regularization by a compressive sensing method

Different from the surface survey, the vertical seismic profile (VSP) survey deploys sources on the surface and geophones in a well. VSP provides higher resolution information of subsurface structures. The faults that cannot be imaged with surface seismic data may be detected with VSP data, and detailed analysis of fracture zones can be achieved with multicomponent VSP. However, one of the main problems is that the sources seldom are acquired on a regular grid in realistic VSP surveys. The irregular samplings cause serious artifacts in migration or imaging, such that data regularization must be implemented first. We have developed a compressive sensing (CS)-based method to regularize nonstationary VSP data. Our method directly operates on irregularly gridded data sets, which is a key contribution compared to the existing CS-based reconstruction methods that work on regular grids. The CS framework consists of a sparsity constraint and a penalty term. We have used the curvelet transform for sparsity constraint of nonstationary events in the regularization term and the nonequispaced Fourier transform to regularize the VSP data in a penalty term. An alternative directional method of multipliers is used for solving the optimization problem. Our method is tested on synthetic, field 2D and 3D VSP data sets. Our method obtains improved reconstructions on continuities of the events and produces fewer artifacts compared to the well-known antileaking Fourier transform method.

Trace Interpolation with Partial CRS-Stacks

This bachelor thesis is about seismic trace interpolation, data regularization and extrapolation using partial CRS stacks. I edit the underlying synthetic data record to create sparse data and prepare for extrapolation. The gaps appear randomly in mid-offset and specifically in near-offset. Subsequently, I compare the results of the interpolation with the traces I deleted from the record, examine data regularization and analyze the extrapolation. The interpolation process preserves arrival times and frequencies very well. Especially the arrival times in near-offset are within minimum tolerance. However some trace contain lowintensity noise over the entire frequency bandwidth. The results show an interpolation error for the direct wave. This error occurs because the linear move-out of the direct wave cannot be approximated by the hyperbolic approach of the partial CRS stack.Data regularization is interpolation for equally spaced intervals. These intervals can be defined precisely and therefore hold the accuracy of the interpolation process.Extrapolation issues a challenge to the partial CRS algorithm. The Fresnel zone defines physical boundaries for the partial CRS apertures. I calculated the extrapolation for more than double of these limitations. Thus, I expect extrapolation artifacts such as amplitude escalation and signal splitting. These expectations are answered by amplitude escalation, smearing and signal splitting, appearing at 1.5 times the Fresnel zone. Nevertheless, extrapolation supplies a reliable extension of the reflection events. For the sake of extrapolation artifacts limitations by the Fresnel zone ought not be breached.In conclusion, I validate seismic trace interpolation and data regularization processes of the partial CRS stack and I point out boundaries of the extrapolation process