Wave refocusing for linear diffractor using the time-reversal principle

One of the problems encountered in a variety of near-surface investigations is detecting and mapping localized heterogeneities. The heterogeneities may be classified under two kinds of objects: (1) a point diffractor that can be considered as an approximation of a small quasi-isometric, such as small karstic cavities and caves; (2) a linear diffractor roughly approximating an elongated object, such as a tube or fault plane. The point and linear diffractors generate two types of seismic diffraction: tip and edge waves, respectively. During the last few decades, different methods were proposed by many researchers for detecting these heterogeneities utilizing seismic waves diffracted by them. An alternative method for detecting point diffractors using a time-reversal principle combined with focusing analysis is proposed in this study: we present an extension of the time-reversal method for linear diffractors. It consists of a coherent summation of seismic energy along edge-diffraction traveltimes. Real data examples show the feasibility and efficiency of the proposed method.     

Post-stack velocity analysis in the dip-angle domain using diffractions

Interval velocity analysis in complex geological areas is often considered as an unresolved problem. A novel approach to improve the velocity analysis process is to perform the analysis in a non-conventional domain and to use seismic events that are usually ignored during standard data processing and imaging. In this study, a method to analyse diffraction data for migration velocity analysis in the time- or depth-domain is presented. The method is based on the clear distinction between diffractions and reflections in the post-migration dip-angle domain. The attractive possibility to perform the analysis, using only stacked data as an input, is demonstrated on synthetic and real data examples.     

Operator-oriented CRS interpolation

In 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‐reflection‐surface 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.     

3D post‐stack interval velocity analysis with effective use of datuming

Interval velocity analysis using post‐stack data has always been a desire, mainly for 3D data sets. In this study we present a method that uses the unique characteristics of migrated diffractions to enable interval velocity analysis from three‐dimensional zero‐offset time data. The idea is to perform a standard three‐dimensional prestack depth migration on stack cubes and generate three‐dimensional common image gathers that show great sensitivity to velocity errors. An efficient ‘top‐down’ scheme for updating the velocity is used to build the model. The effectiveness of the method is related to the incorporation of wave equation based post‐stack datuming in the model building process. The proposed method relies on the ability to identify diffractions along redatumed zero‐offset data and to analyse their flatness in the migrated local angle domain. The method can be considered as an additional tool for a complete, prestack depth migration based interval velocity analysis.     

Multiple prediction using the homeomorphic-imaging technique

A new method for predicting different kinds of multiples and peg-leg reflections in unstacked seismic data is discussed. The basis for this method is the fact that kinematic properties of multiples can be represented as a combination of kinematic properties of primary reflections. The prediction is made using a two-step process. In the first step, the values for the angle of emergence and radius of curvature of the wavefront for primary reflections from ‘multiple-generating’ interfaces are obtained. These parameters are estimated directly from unstacked data for every source point using the homeomorphic-imaging technique. The second step consists of prediction of multiples from primary reflections that satisfy a so-called ‘multiple condition’. This condition is the equality of the absolute values of the angles of emergence calculated from the first step. This method is effective even in complex media and information on the subsurface geology is not required. The parameters are estimated directly from the unstacked data and do not require any computational efforts such as in wavefield extrapolation of data.