Application of supergrouping to land seismic data in desert environment

Bakulin, Andrey; Golikov, Pavel; Dmitriev, Maxim; Dolgov, Victor; Neklyudov, Dmitry

doi:10.1190/segam2016-13838765.1

Cited by 15 publications

(10 citation statements)

References 1 publication

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“…To address the first limitation, we apply supergrouping after normal moveout corrections using preliminary velocity information, which is always available. It allows us to preserve signals at higher frequencies and use larger summation apertures (Bakulin et al, 2016). The second limitation is addressed by using a diversity stack (Martinez et al, 1993) that weights each trace by its smoothed envelope before summation, whereas the final sum is then normalized by the sum of the original weights:…”

Section: Methodsmentioning

confidence: 99%

“…Proper estimation of these parameters is a prerequisite for any further processing. Supergrouping was introduced by Neklyudov et al (2015) to effectively address these issues and Bakulin et al (2016) showed various field examples demonstrating uplift in processing. Initial implementation was not optimal to support many iterative runs of flexible supergrouping on huge datasets.…”

Section: Introductionmentioning

confidence: 99%

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Efficient four-dimensional supergrouping algorithm for enhancement of high-channel count seismic data

Dmitriev

Bakulin

Golikov

2017

SEG Technical Program Expanded Abstracts 2017

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Modern seismic acquisition is trending toward recording high-channel count data with smaller field arrays or single sensors. Reducing the size of field arrays leads to a significant degradation of data quality that needs to be dealt with during processing. We present a computationally efficient approach for four-dimensional supergrouping of shots and receivers that significantly improves the pre-stack signal-to-noise ratio. This algorithm allows enhancement of 1.5 TB of data in less than 10 hours using a single compute node, and so enabling efficient iterative application required for modern high-channel count seismic data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient four-dimensional supergrouping algorithm for enhancement of high-channel count seismic data

Dmitriev

Bakulin

Golikov

2017

SEG Technical Program Expanded Abstracts 2017

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show abstract

“…Denser 3D data opens new opportunities. Bakulin et al (2016) showed that supergrouping allows enhancement of the reflected signal and obtains more reliable estimates of prestack parameters from enhanced data (velocities, deconvolution operator, statics) and, finally, gives a much better image in comparison with conventional single-sensor processing. Application of NMO corrections prior to supergrouping allows us to handle larger spatial separation between traces.…”

Section: Supergroupingmentioning

confidence: 99%

“…Proper estimation of these parameters is a prerequisite for any further processing. Promising results of enhancement of very challenging data were obtained recently by supergrouping of neighboring traces (Bakulin et al, 2016). While this approach is robust and fast, it assumes summation is done along global hyperbolic normal moveout (NMO) and, as such, can be applied in areas with relatively simple geological structure.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Challenging Prestack Data Using Local Summation Approaches

Dmitriev¹,

Bakulin²,

Silvestrov³

et al. 2018

Proceedings

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For high-channel count and single-sensor seismic data many processing steps requiring estimation of prestack parameters become more challenging due to the low signal-to-noise ratio of the data. Conventional processing algorithms involve estimation of velocities, statics and surface consistent scalars and operators, and need good prestack data quality, which is rarely the case for land seismic data acquired in arid desert environments of Saudi Arabia with a complex near surface. We present two methods for prestack seismic signal enhancement based on mixing neighboring traces. The first method called supergrouping performs local summation of traces using a global normal moveout correction to align reflected signal. The second approach, called nonlinear beamforming (NLBF), is a data-driven procedure to estimate local moveout directly from the data. We demonstrate the signal enhancement ability of these procedures on synthetic and challenging land seismic data from Saudi Arabia.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Application of supergrouping to land seismic data in desert environment

Cited by 15 publications

References 1 publication

Efficient four-dimensional supergrouping algorithm for enhancement of high-channel count seismic data

Efficient four-dimensional supergrouping algorithm for enhancement of high-channel count seismic data

Enhancing Challenging Prestack Data Using Local Summation Approaches

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

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