RTM Noise Attenuation and Image Enhancement Using Time-shift Gathers

Khalil, A.; Sun, James; Zhang, Y.; Poole, Gordon

doi:10.3997/2214-4609.20141485

Cited by 6 publications

(6 citation statements)

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“…Stable propagation of an acoustic TI P-wave requires ε > δ in order to keep the medium stiffness matrix positive definite, as required by the medium energy constraint. Moreover, it is necessary to suppress unwanted S-wave arrivals before applying the imaging condition (Zhang et al 2009;Khalil et al 2013) to reduce crosstalk noise in the final image. For S-wave propagation in RTM, the acoustic P-wave formulation can be adapted to weak anisotropy as previously described, but there is no such adaptation for strong anisotropy.…”

Section: S -W a V E M O D E L I N G A N D M I G R A T I O N F O R W Ementioning

confidence: 99%

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Converted‐wave beam migration with sparse sources or receivers

Casasanta¹,

Gray²

2015

Geophysical Prospecting

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Gaussian beam depth migration overcomes the single‐wavefront limitation of most implementations of Kirchhoff migration and provides a cost‐effective alternative to full‐wavefield imaging methods such as reverse‐time migration. Common‐offset beam migration was originally derived to exploit symmetries available in marine towed‐streamer acquisition. However, sparse acquisition geometries, such as cross‐spread and ocean bottom, do not easily accommodate requirements for common‐offset, common‐azimuth (or common‐offset‐vector) migration. Seismic data interpolation or regularization can be used to mitigate this problem by forming well‐populated common‐offset‐vector volumes. This procedure is computationally intensive and can, in the case of converted‐wave imaging with sparse receivers, compromise the final image resolution. As an alternative, we introduce a common‐shot (or common‐receiver) beam migration implementation, which allows migration of datasets rich in azimuth, without any regularization pre‐processing required. Using analytic, synthetic, and field data examples, we demonstrate that converted‐wave imaging of ocean‐bottom‐node data benefits from this formulation, particularly in the shallow subsurface where regularization for common‐offset‐vector migration is both necessary and difficult.

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Section: S -W a V E M O D E L I N G A N D M I G R A T I O N F O R W Ementioning

confidence: 99%

“…; Khalil et al . ) to reduce crosstalk noise in the final image. For S‐wave propagation in RTM, the acoustic P‐wave formulation can be adapted to weak anisotropy as previously described, but there is no such adaptation for strong anisotropy.…”

Section: S‐wave Modeling and Migration For Weak And Strong Anisotropymentioning

confidence: 99%

Converted‐wave beam migration with sparse sources or receivers

Casasanta¹,

Gray²

2015

Geophysical Prospecting

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“…To separate the incident and reflection wavefields in both the vertical and horizontal directions, Irabor and Warner (2016) propose to use a pair of 1D two‐way wave equations to separately simulate the vertical and horizontal wave propagations. The second strategy is to apply scattering angle filtering, based on the assumption that the tomographic component corresponds to the scattering angle close to 180° (Alkhalifah, 2014; Khalil et al., 2014; Wu & Alkhalifah, 2015, 2017; Yao et al., 2018, 2019). The third strategy is to apply Born modeling to separate tomographic and migration components (Sun et al., 2017; Wu & Alkhalifah, 2017; Xu et al., 2012; Yao & Wu, 2017).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Feedback Convolutional‐Neural‐Network‐Based High‐Resolution Reflection‐Waveform Inversion

McMechan

Wang

2022

JGR Solid Earth

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is a powerful algorithm to perform a least squares non-linear data-fitting optimization to estimate a high-resolution velocity model. The conventional FWI gradient, obtained by zero-lag cross-correlating both the source and residual wavefields, can be divided into three components (Alkhalifah, 2014;Wu & Alkhalifah, 2015;Xu et al., 2012;Yao et al., 2020): (a) the low-wavenumber components obtained from the direct, refracted, and diving waves, (b) the low-wavenumber (tomographic) components associated with reflections and (c) the high-wavenumber (migration) components associated with reflections. Because of the shallower penetration depth of the direct, refracted, and diving waves (relative to the reflected waves) and also because of the overlapping of their wavepaths in both the source and residual

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“…Khalil et al () proposed a method to attenuate the low‐wave number noise in reverse‐time migration (RTM) with time‐shift gathers under the assumption of isotropic velocity. Since the low‐wave number noise of RTM is the tomography component in the gradient of FWI effectively, this method was then further used to separate the modes of FWI with angle filtering (Alkhalifah, ; Z. Wu & Alkhalifah, ; Z. Wu & Alkhalifah, ).…”

Section: Introductionmentioning

confidence: 99%

Separation of Migration and Tomography Modes of Full‐Waveform Inversion in the Plane Wave Domain

et al. 2018

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Full‐waveform inversion (FWI) includes both migration and tomography modes. The migration mode acts like a nonlinear least squares migration to map model interfaces with reflections, while the tomography mode behaves as tomography to build a background velocity model. The migration mode is the main response of inverting reflections, while the tomography mode exists in response to inverting both the reflections and refractions. To emphasize one of the two modes in FWI, especially for inverting reflections, the separation of the two modes in the gradient of FWI is required. Here we present a new method to achieve this separation with an angle‐dependent filtering technique in the plane wave domain. We first transform the source and residual wavefields into the plane wave domain with the Fourier transform and then decompose them into the migration and tomography components using the opening angles between the transformed source and residual plane waves. The opening angles close to 180° contribute to the tomography component, while the others correspond to the migration component. We find that this approach is very effective and robust even when the medium is relatively complicated with strong lateral heterogeneities, highly dipping reflectors, and strong anisotropy. This is well demonstrated by theoretical analysis and numerical tests with a synthetic data set and a field data set.

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RTM Noise Attenuation and Image Enhancement Using Time-shift Gathers

Cited by 6 publications

References 0 publications

Converted‐wave beam migration with sparse sources or receivers

Converted‐wave beam migration with sparse sources or receivers

Adaptive Feedback Convolutional‐Neural‐Network‐Based High‐Resolution Reflection‐Waveform Inversion

Separation of Migration and Tomography Modes of Full‐Waveform Inversion in the Plane Wave Domain

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