An Efficient Least–Squares Migration

Leggott, Richard; Wombell, R.; Conroy, Graham; Noss, Tony; Williams, Geoffrey Lee

doi:10.3997/2214-4609.201401856

Cited by 1 publication

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“…The following NMO‐based approach is the time‐domain equivalent of wave‐equation‐based extrapolation workflows (e.g. Fomel, 2003; Leggott et al ., 2007) that relies on some prior knowledge of seismic velocities. It is worth mentioning that the NMO‐based approach also requires some prior information about the subsurface geological setting to know if the media is isotropic or anisotropic, while it is not a requirement for the presented oriented extrapolation workflow.…”

Section: Field Data Examplementioning

confidence: 99%

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Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

Khoshnavaz

2022

Geophysical Prospecting

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Seismic reconstruction of missing traces is an extremely important subject in seismic data processing. It includes both interpolation and extrapolation of sparsely recorded data. Extrapolation is often performed in the absence of near-offset seismic data recorded through marine acquisition. Several reconstruction methods have been designed to circumvent this sparsity in time-offset, frequency-offset and timefrequency domains. In this research, I propose an oriented extrapolation workflow to reconstruct near-offset missing traces. The term oriented or velocity-independent refers to those techniques that are based on the use of local slopes. In the proposed workflow, I use an oriented time-warping algorithm called predictive painting. This algorithm is suitable to predict two-way traveltimes between two distinctive points of an event. Seismic events recorded by an off-end array very rarely contain dips of both signs with respect to their zero-offset location in common-midpoint domain. This makes the domain an ideal choice to run the algorithm. The proposed algorithm is demonstrated on synthetic and field data examples. I decimate near-offset seismic traces and reconstruct them through the algorithm. The reconstruction results are compared with the original data before decimation. Furthermore, insensitivity of the proposed workflow to the presence of class II amplitude-versus-offset anomalies is demonstrated on a synthetic example. I also perform a velocity-dependent (a normalmoveout-based) technique on the field data and compare the corresponding outcomes with the results achieved by the application of the proposed velocity-independent approach. All the results suggest that the proposed technique has the potential to be used in the exploration industry.

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Section: Field Data Examplementioning

confidence: 99%

“…Techniques developed by Ronen (1987), Stolt (2002), Fomel (2003) and Leggott et al . (2007) are recognized under the wave‐equation‐based reconstruction category. Signal‐processing reconstruction techniques are mostly based on the transformation of data into other domains.…”

Section: Introductionmentioning

confidence: 99%

Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

Khoshnavaz

2022

Geophysical Prospecting

View full text Add to dashboard Cite

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An Efficient Least–Squares Migration

Cited by 1 publication

References 0 publications

Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

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