RTM angle gathers using Poynting vectors

Dickens, Thomas A.; Winbow, Graham A.

doi:10.1190/1.3627841

Cited by 68 publications

(18 citation statements)

References 9 publications

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“…The same procedure and format can also be used to select and/or display the wave amplitudes over time at a grid point in the reverse-time migration for any source or receiver propagation angles, or the incident, reflected, or opening angles for P-or S-waves (i.e., the source, receiver, or migrated wavefields). If the P and S data are separated before (or during) migration, ADCIGs can be constructed separately for P and converted S reflections; for 2C or 3C elastic data, the propagation angles of both source and receiver wavefields are available directly from the stress and particle motion vector components, via the definition of the Poynting vector (e.g., Dickens and Winbow, 2011), eliminating the need for complicated and expensive extraction of propagation directions to calculate the incident or reflection angles (Wang and McMechan 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Ray-based prestack migration velocity analysis (e.g., Stork 1992;Wang, Pann, and Meek 1995;Liu 1997;Montel and Lambaré 2013) is efficient, but the amplitude and angle information can be affected by the limitations of ray theory in complex media (Operto et al 2000;Xu et al 2001). Three different forms of wave-equation-based ADCIG algorithms have been proposed: direction-vector-based methods (e.g., Yoon et al 2011;Zhang and McMechan 2011a,b;Dickens and Winbow 2011;Vyas, Nichols, and Mobley 2011), local plane-wave decomposition methods (e.g., Xie and Wu 2002;Xu et al 2011;Xie 2011, 2012), and local-shift imaging condition methods (e.g., Sava andFomel 2003, 2005a,b;Xu et al 2014). Wave-equation-based images have more reliable waveform information (Zhang, Zhang, and Bleistein 2005), but calculating the angle apertures requires a substantial extra computational effort (e.g., Jin and McMechan 2014).…”

Section: U L T I P a T H S I N A N G L E -D O M A I N C O M M O N -mentioning

confidence: 99%

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Multipathing in three‐parameter common‐image gathers from reverse‐time migration

Zhang

Zhang²,

McMechan

et al. 2016

Geophysical Prospecting

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Reverse‐time migration gives high‐quality, complete images by using full‐wave extrapolations. It is thus not subject to important limitations of other migrations that are based on high‐frequency or one‐way approximations. The cross‐correlation imaging condition in two‐dimensional pre‐stack reverse‐time migration of common‐source data explicitly sums the product of the (forward‐propagating) source and (backward‐propagating) receiver wavefields over all image times. The primary contribution at any image point travels a minimum‐time path that has only one (specular) reflection, and it usually corresponds to a local maximum amplitude. All other contributions at the same image point are various types of multipaths, including prismatic multi‐arrivals, free‐surface and internal multiples, converted waves, and all crosstalk noise, which are imaged at later times, and potentially create migration artefacts. A solution that facilitates inclusion of correctly imaged, non‐primary arrivals and removal of the related artefacts, is to save the depth versus incident angle slice at each image time (rather than automatically summing them). This results in a three‐parameter (incident angle, depth, and image time) common‐image volume that integrates, into a single unified representation, attributes that were previously computed by separate processes. The volume can be post‐processed by selecting any desired combination of primary and/or multipath data before stacking over image time. Separate images (with or without artifacts) and various projections can then be produced without having to remigrate the data, providing an efficient tool for optimization of migration images. A numerical example for a simple model shows how primary and prismatic multipath contributions merge into a single incident angle versus image time trajectory. A second example, using synthetic data from the Sigsbee2 model, shows that the contributions to subsalt images of primary and multipath (in this case, turning wave) reflections are different. The primary reflections contain most of the information in regions away from the salt, but both primary and multipath data contribute in the subsalt region.

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Section: Discussionmentioning

confidence: 99%

Section: U L T I P a T H S I N A N G L E -D O M A I N C O M M O N -mentioning

confidence: 99%

Multipathing in three‐parameter common‐image gathers from reverse‐time migration

Zhang

Zhang²,

McMechan

et al. 2016

Geophysical Prospecting

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“…We evaluate images of the singularities for multiple sources and rearrange them in terms of local incidence angle (image gather) [13,34,35,37]. In Fig.…”

Section: Restricted Angle Transformmentioning

confidence: 99%

Multiscale Reverse-Time-Migration-Type Imaging Using the Dyadic Parabolic Decomposition of Phase Space

Андерссон¹,

Hoop²,

Wendt³

2015

SIAM J. Imaging Sci.

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We develop a representation of reverse-time migration in terms of Fourier integral operators the canonical relations of which are graphs. Through the dyadic parabolic decomposition of phase space, we obtain the solution of the wave equation with a boundary source and homogeneous initial conditions using wave packets. On this basis, we develop a numerical procedure for the reversetime continuation from the boundary of scattering data and for RTM migration. The algorithms are derived from those we recently developed for the discrete approximate evaluation of the action of Fourier integral operators and inherit from their conceptual and numerical properties.

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“…To address this difficulty, Wang et al (2011) and Dickens and Winbow (2011) employed Poynting vectors to extract ADCIGs in RTM, in which they calculated the angles between the Poynting vectors of source and receiver wavefields and further obtained the incident angles. Also, it is necessary to take the resolution of relevant angles and imaging quality into consideration.…”

Section: Introductionmentioning

confidence: 99%

“…Also, it is necessary to take the resolution of relevant angles and imaging quality into consideration. To address this difficulty, Wang et al (2011) and Dickens and Winbow (2011) employed Poynting vectors to extract ADCIGs in RTM, in which they calculated the angles between the Poynting vectors of source and receiver wavefields and further obtained the incident angles. This method is efficient in extracting ADCIGs.…”

Section: Introductionmentioning

confidence: 99%

P‐ and S‐wave angle‐domain common‐image gathers (ADCIGs) based on elastic‐wave reverse‐time migration

Haibo

2016

Geological Journal

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Elastic‐wave reverse‐time migration is an important imaging strategy because of its ability to image complex geologic structures and to efficiently extract elastic‐wave angle‐domain common‐image gathers (ADCIGs), which is an important task for amplitude‐versus‐angle (AVA) analysis and migration velocity analysis (MVA) in multicomponent exploration. Based on the characteristics of Poynting vectors in an elastic medium, we derive the wavefield‐separation imaging conditions for elastic reverse‐time migration (RTM) and establish the method of calculating the incident angle of source P‐wavefield using the propagation angle of source P‐wavefield and local dip angle of the reflector. Accurate ADCIGs can then be extracted based on the estimated incident angles with small computational cost. Two numerical examples show that the proposed method for extracting elastic‐wave ADCIGs can provide clear and accurate image and image gathers for elastic‐wave imaging tasks and therefore can provide reliable amplitude information for AVA and MVA. Copyright © 2016 John Wiley & Sons, Ltd.

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RTM angle gathers using Poynting vectors

Cited by 68 publications

References 9 publications

Multipathing in three‐parameter common‐image gathers from reverse‐time migration

Multipathing in three‐parameter common‐image gathers from reverse‐time migration

Multiscale Reverse-Time-Migration-Type Imaging Using the Dyadic Parabolic Decomposition of Phase Space

P‐ and S‐wave angle‐domain common‐image gathers (ADCIGs) based on elastic‐wave reverse‐time migration

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