Application of supergrouping to enhance 3D prestack seismic data from a desert environment

Fit-for-purpose enhancement remains a critical task for processing land seismic data, especially with the increasing popularity of high-channel-count and single-sensor data. We describe here a flexible scheme called smart supergrouping that performs summation of traces from neighboring shots and receivers. Supergrouping enhances desired reflection signals while suppressing ground roll and other noise. It also delivers prestack data of significantly high quality, critical for deriving velocities, deconvolution operators, scalars, and statics, as well as for improved imaging. While similar in concept to field source/receiver array forming, supergrouping may be applied to data already acquired with field arrays. Unlike field arrays, supergrouped data have kinematic corrections applied with overlapping apertures. We also have the ability to compensate for intra-array statics and wavelet variations. We demonstrate the signal-enhancing abilities of such generalized supergroups with normal-moveout corrections on challenging land and ocean-bottom-cable seismic data from Saudi Arabia. Applications of supergrouped data cover various steps of land seismic processing from first-break picking to deconvolution to statics to full-waveform inversion and imaging. Introduction Land seismic data from desert regions are known to have low signal-to-noise ratio (S/N) (Robinson and Al-Husseini, 1982). Modern seismic acquisition is trending toward recording a higher number of channels to increase spatial sampling. For economic reasons, high channel counts come at the expense of using smaller receiver arrays or single sensors. Likewise, a similar tradeoff applies on the source side where increased spatial sampling is also accompanied by reduced size of field source arrays. In areas with a complex near surface such as Saudi Arabia, this means acquiring data of higher density but significantly lower prestack quality. Naturally, every processing step that relies on prestack signal becomes challenging to execute. Signal processing can help but only if the signal present on each trace is above a certain minimum useful threshold. Unfortunately, a lot of acquired data remains below such a threshold. In this case, collecting signal from neighboring traces becomes perhaps the only practical option. It is well established that source and receiver arrays in the field are capable of greatly improving S/N, and they are still widely used. Since there is no practical way to introduce any kinematic corrections in the field, such arrays require fine spatial sampling. The use of field arrays in acquisition has been greatly reduced following the rise in popularity of single-sensor acquisition. The spatial sampling of modern high-channel-count or single-sensor data remains significantly larger than what is required for conventional group forming. This creates a challenge for seismic Andrey Bakulin1, Pavel Golikov1, Maxim Dmitriev1, Dmitry Neklyudov2, Pierre Leger3, and Viktor Dolgov3 processing that can no longer rely on field arrays or digital group forming because of large spatial sampling. In this study, we propose a method of enhancing the quality of conventional 3D land prestack data using a supergrouping technique (Bakulin et al., 2016) that combines elements of grouping and stacking. With increased emphasis on low frequencies and proliferation of hierarchical techniques (applied from progressively low to high frequencies) from statics to velocity model building, we expect that adaptive supergrouping may fill the missing gap for different frequency bands. Supergrouping builds on group forming but goes beyond that to deal with large source/receiver intervals, using simple assumptions and smart summation techniques proven to work well for field data with low S/N from a variety of different terrains. In this paper, we briefly describe the methodology and show applications using various seismic data sets from Saudi Arabia. Method Algorithm. Supergrouping is typically applied to input data that have already been recorded with source/receiver arrays, hence the use of the prefix “super. ” It is similar to conventional group forming in that traces are summed within the group, but there are significant differences. Most importantly, in contrast to field arrays, in processing we can apply additional moveout and amplitude/ phase corrections before summation. In addition, supergrouping is designed to operate on 3D field data and handle irregular geometries. For these reasons, preconditioning of the data is not trivial, as it may require individual adjustments for every supergroup as well as some regularization. Supergroups can be implemented in the source domain, receiver domain, cross-spread domain, or any combination, with the most general supergroup being four-dimensional. In all examples presented here, the output geometry is identical to the input, although regular coarser geometry may also be acceptable for many processing steps. Therefore, this could be thought of as a sliding spatial window in which an enhanced trace is output at the “central” location of the supergroup aperture (Figure 1). We use a simple naming convention for such supergroups as number of shots inline × number of shots crossline × number of receivers inline × number of receivers crossline. For instance, the group in Figure 1 comprises summation of 3 by 3 shots for each receiver and 3 by 3 receivers for each shot, thus it is referred to as a 3 × 3 × 3 × 3 supergroup. Likewise, as shown in Figure 1, the area of the supergroup is defined by multiplying by the grid size in each dimension. There is half a grid size perimeter zone around sources and receivers similar to that used for classical group forming. Stacking itself can be achieved with a multitude of methods including straight summation (a simple stack), diversity stacking, and weighted stacking. A simple stack denotes summing traces from the supergroup ensemble typically with moveout corrections 1Saudi Aramco, EXPEC Advanced Research Center. 2Institute of Petroleum Geology and Geophysics SB RAS. 3Saudi Aramco, Geophysical Data Processing Division. https://doi.org/10.1190/tle37030200.1. D ow nl oa de d 03 /0 9/ 18 to 7 3. 25 5. 13 . 1 67 . 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