We introduce a generalized concept of the so-called blending and deblending, and establish the generalizedblending and -deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent shooting, inhomogeneous shooting, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works for all shot-generated-wavefields separation, spectrum recovery and balancing, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this challenge, we consider an iterative optimization scheme using a so-called closed-loop approach. We use the properties of blended signal specified by the blending code: the coherency of blended signal versus the incoherency of blending noise in the pseudo-deblended domain. This can be posed as an inverse problem with quantifying the coherency and its solutions by selecting optimal metrics of the coherency. We applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. We discuss its applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Our methodology should reduce the repeatability problem because reconstructing deblended data in monitor surveys is much more realistic and reliable than positioning sources and receivers exactly as the baseline survey.

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Three-dimensional seismic survey design should provide an acquisition geometry that enables imaging and amplitude-versus-offset applications of target reflectors with sufficient data quality under given economical and operational constraints. However, in land or shallow-water environments, surface waves are often dominant in the seismic data. The effectiveness of surface-wave separation or attenuation significantly affects the quality of the final result. Therefore, the need for surface-wave attenuation imposes additional constraints on the acquisition geometry. Recently, we have proposed a method for surface-wave attenuation that can better deal with aliased seismic data than classic methods such as slowness/velocity-based filtering. Here, we investigate how surface-wave attenuation affects the selection of survey parameters and the resulting data quality. To quantify the latter, we introduce a measure that represents the estimated signal-to-noise ratio between the desired subsurface signal and the surface waves that are deemed to be noise. In a case study, we applied surface-wave attenuation and signal-to-noise ratio estimation to several data sets with different survey parameters. The spatial sampling intervals of the basic subset are the survey parameters that affect the performance of surface-wave attenuation methods the most. Finer spatial sampling will reduce aliasing and make surface-wave attenuation easier, resulting in better data quality until no further improvement is obtained. We observed this behaviour as a main trend that levels off at increasingly denser sampling. With our method, this trend curve lies at a considerably higher signal-to-noise ratio than with a classic filtering method. This means that we can obtain a much better data quality for given survey effort or the same data quality as with a conventional method at a lower cost.@en

For 3-D shallow-water seismic surveys offshore Abu Dhabi, imaging the target reflectors requires high resolution. Characterization and monitoring of hydrocarbon reservoirs by seismic amplitude-versus-offset techniques demands high pre-stack amplitude fidelity. In this region, however, it still was not clear how the survey parameters should be chosen to satisfy the required data quality. To answer this question, we applied the focal-beam method to survey evaluation and design. This subsurface- and target-oriented approach enables quantitative analysis of attributes such as the best achievable resolution and pre-stack amplitude fidelity at a fixed grid point in the subsurface for a given acquisition geometry at the surface. This method offers an efficient way to optimize the acquisition geometry for maximum resolution and minimum amplitude-versus-offset imprint. We applied it to several acquisition geometries in order to understand the effects of survey parameters such as the four spatial sampling intervals and apertures of the template geometry. The results led to a good understanding of the relationship between the survey parameters and the resulting data quality and identification of the survey parameters for reflection imaging and amplitude-versus-offset applications.@en

Surface waves in seismic data are often dominant in a land or shallow-water environment. Separating them from primaries is of great importance either for removing them as noise for reservoir imaging and characterization or for extracting them as signal for near-surface characterization. However, their complex properties make the surface-wave separation significantly challenging in seismic processing. To address the challenges, we propose a method of three-dimensional surface-wave estimation and separation using an iterative closed-loop approach. The closed loop contains a relatively simple forward model of surface waves and adaptive subtraction of the forward-modelled surface waves from the observed surface waves, making it possible to evaluate the residual between them. In this approach, the surface-wave model is parameterized by the frequency-dependent slowness and source properties for each surface-wave mode. The optimal parameters are estimated in such a way that the residual is minimized and, consequently, this approach solves the inverse problem. Through real data examples, we demonstrate that the proposed method successfully estimates the surface waves and separates them out from the seismic data. In addition, it is demonstrated that our method can also be applied to undersampled, irregularly sampled, and blended seismic data.@en