The importance of non-hyperbolic and stretch effects in far-offset P and PS NMO processing

Strong, Shaun; Hearn, Steve

doi:10.1071/aseg2012ab092

Cited by 3 publications

(6 citation statements)

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“…For the specific 3D-3C trial considered here, a reasonable balance of these was achieved. The overall results of the trial are presented elsewhere (Strong and Hearn, 2011). The offset-azimuth distribution achieved across the survey has permitted the detection of clear azimuthal anisotropy in both P and PS data, with convincing relationships to fault structures and associated stress directions (Hearn and Strong, 2012).…”

Section: Resultsmentioning

confidence: 93%

“…Note that further subtle resolution improvement can be obtained by using more 3D-PS Survey Design advanced PS-NMO algorithms (e.g. Strong and Hearn, 2012). These field observations add weight to the modeling results, and sugggest that PS events at offsets greater than the bottom-of-coal critical angle may be usable, provided the offset range is carefully chosen so as to maintain reasonably consistent character in the seam reflection package.…”

Section: Offset Considerations For Coal-scale Ps Designmentioning

confidence: 90%

“…For P-wave coal exploration, 3D surveys are now relatively common, providing detailed spatial interpretation critical for mine planning. As a logical extension to these trends, a recent trial (Strong and Hearn, 2011) has examined the feasibility and value of a 3D-3C survey over a coal target previously explored using 2D P-wave seismic and limited drilling. The overall objectives were to optimize 3D acquisition and processing of integrated P and PS data at the coal scale.…”

Section: Introduction Motivation For This Studymentioning

confidence: 99%

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Survey design for coal-scale 3D-PS seismic reflection

Strong

Hearn

2016

GEOPHYSICS

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Survey design for converted-wave (PS) reflection is more complicated than for standard P-wave surveys, due to ray-path asymmetry and increased possibility of phase distortion. Coal-scale PS surveys (depth < 300m) require particular consideration, partly due to the particular physical properties of the target (low density and low velocity). Finite-difference modeling provides a pragmatic evaluation of the likely distortion due to inclusion of post-critical reflections. If the offset range is carefully chosen then it may be possible to incorporate high-amplitude post-critical reflections without seriously degrading the resolution in the stack. Offsets of up to three times target depth may in some cases be usable, with appropriate quality control at the data-processing stage. This means that the PS survey design may need to handle ray paths which are highly asymmetrical, and which are very sensitive to assumed velocities. A 3D-PS design case study is included for a particular coal survey with target in the depth range 85-140m. The objectives were acceptable fold balance between bins, and relatively smooth distribution of offset and azimuth within bins. These parameters are relatively robust for the P-wave design, but much more sensitive for the case of PS. Reduction of the source density is more acceptable than reduction of receiver density, particularly in terms of offset-azimuth distribution. This is a fortuitous observation in that it improves the economics of a dynamite source, which is desirable for highresolution coalmine planning. The final survey design necessarily allowed for logistical and economic considerations, which implied some technical compromise. Nevertheless good fold, offset and azimuth distributions were achieved across the survey area, yielding a dataset suitable for meaningful analysis of P and S azimuthal anisotropy.

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Section: Resultsmentioning

confidence: 93%

Section: Offset Considerations For Coal-scale Ps Designmentioning

confidence: 90%

Section: Introduction Motivation For This Studymentioning

confidence: 99%

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Survey design for coal-scale 3D-PS seismic reflection

Strong

Hearn

2016

GEOPHYSICS

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“…Finally, a great deal of care is needed in the amount of isomoveout curve utilised for each reflector. Strong and Hearn (2012) extend the CNMO method for compression (P)-and converted (PS)-wave nonhyperbolic moveout based on the non-hyperbolic moveout approximation of Tsvankin and Thomsen (1994). Despite being applicable for non-hyperbolic moveout correction and converted wave seismic datasets, The approach of Strong and Hearn (2012) still suffers from cross-reflection artefacts that result from stretch-free moveout.…”

Section: Moveout Correctionmentioning

confidence: 99%

“…Strong and Hearn (2012) extend the CNMO method for compression (P)-and converted (PS)-wave nonhyperbolic moveout based on the non-hyperbolic moveout approximation of Tsvankin and Thomsen (1994). Despite being applicable for non-hyperbolic moveout correction and converted wave seismic datasets, The approach of Strong and Hearn (2012) still suffers from cross-reflection artefacts that result from stretch-free moveout. Zhang et al (2013) develop the matching pursuit normal moveout (MPNMO) approach to remove stretch from the data as well as artefacts.…”

Section: Moveout Correctionmentioning

confidence: 99%

A general framework for anisotropic and wavefront moveout parameter analysis

Wilson¹

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Seismic data processing is essential in the development of seismic images. Seismic images are utilised for interpretation purposes in which insights into the underlying geology and structure are obtained. In the search for resource plays, imaging and interpretation are essential. To correctly image the subsurface, there are many important steps in a typical seismic data processing workflow. Of particular interest in this thesis are the moveout analysis, moveout correction and stacking processes. Moveout analysis involves the determination of moveout parameters that optimise the performance of the moveout correction and stacking processes. Moveout correction utilises the moveout parameters by using them in a model which transforms the spatial and temporal location of reflection impulses in the underlying seismic gathers so that they are optimally aligned in preparation for stacking. Stacking involves applying a weighted sum (typically the arithmetic mean) along the spatial axis of the underlying seismic gather. Limitations in these processes can lead to erroneous results which ultimately affect the quality of the seismic imaging and interpretation. Moveout analysis performance is determined by the presence of amplitude variation with offset (AVO) anomalies, the fold and quality of the data, the choice of moveout approximation and the number of moveout parameters, the method of moveout parameter determination, and the resolution provided by the semblance operator. Moveout correction performance is controlled by choice of moveout approximation and the method of stretch removal utilised. Stacking performance is dependent on fold, noise, quality of moveout correction and moveout stretch. Financial support No financial support was provided to fund this research.

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Nonhyperbolic normal moveout stretch correction with deep learning automation

Abedi

Pardo

2022

GEOPHYSICS

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Normal moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching as an undesired byproduct. We address the NMO stretching problem with two methods: 1) an exact stretch-free NMO correction that prevents the stretching of primary reflections, and 2) an approximate post-NMO stretch correction. Our stretch-free NMO produces parallel moveout trajectories for primary reflections. Our post-NMO stretch correction calculates the moveout of stretched wavelets as a function of offset. Both methods are based on the generalized moveout approximation and are suitable for application in complex anisotropic or heterogeneous environments. We use new moveout equations and modify the original parameter functions to be constant over the primary reflections, and then interpolate the seismogram amplitudes at the calculated traveltimes. For fast and automatic modification of the parameter functions, we use deep learning. We design a deep neural network (DNN) using convolutional layers and residual blocks. To train the DNN, we generate a set of 40,000 synthetic NMO corrected common midpoint gathers and the corresponding desired outputs of the DNN. The data set is generated using different velocity profiles, wavelets, and offset vectors, and includes multiples, ground roll, and band-limited random noise. The simplicity of the DNN task a 1D identification of primary reflections improves the generalization in practice. We use the trained DNN and show successful applications of our stretch-correction method on synthetic and different real data sets.

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The importance of non-hyperbolic and stretch effects in far-offset P and PS NMO processing

Cited by 3 publications

References 4 publications

Survey design for coal-scale 3D-PS seismic reflection

Survey design for coal-scale 3D-PS seismic reflection

A general framework for anisotropic and wavefront moveout parameter analysis

Nonhyperbolic normal moveout stretch correction with deep learning automation

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