Wave‐equation migration with dithered plane waves

Perrone, Francesco; Sava, Paul

doi:10.1111/j.1365-2478.2011.01011.x

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…First, because we are using only pairs of experiments, the signal-to-noise ratio can be low and the effectiveness of the algorithm may be hampered by the quality of the data. The sensitivity to the signal-to-noise ratio can be addressed using shot-encoding techniques (Whitmore, 1995;Zhang et al, 2005;Morton and Ober, 1998;Soubaras, 2006;Perrone and Sava, 2012) under the assumption that the different experiments are contaminated by uncorrelated noise. Second, because of cycle skipping between images, our approach can be ineffective if the shots are too far from each other, the velocity model is very inaccurate, or the reflector geometry is complex (e.g., around fault zones or in areas with conflicting dips).…”

Section: A) B)mentioning

confidence: 99%

Linearized wave-equation migration velocity analysis by image warping

et al. 2014

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Seismic imaging produces images of contrasts in physical parameters in the subsurface, e.g., velocity or impedance. To build such images, a background model describing the wave kinematics in the earth is necessary. In practice, the structural image and background velocity model are unknown and have to be estimated from the acquired data. Migration velocity analysis deals with estimation of the background model in the framework of seismic migration and relies on two main elements: data redundancy and invariance of the structures with respect to different seismic experiments. Because all the experiments probe the same model, the reflectors must be invariant in suitable domains (e.g., shots or reflection angle); the semblance principle is the tool used to measure the invariance of a set of multiple images. We measure the similarity of the structural features between pairs of single-shot migrated images obtained from adjacent experiments. By using the estimated warping vector field between two migrated images, we construct an image perturbation which describes the difference in reflectivity observed by two shots. We derive an expression for the image perturbation that drives a migration velocity analysis procedure based on a linearization of the wave-equation with respect to the model parameters. Synthetic 2D examples show promising results in retrieving errors in the velocity model. This methodology can be directly applied to 3D.

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Section: A) B)mentioning

confidence: 99%

Linearized wave-equation migration velocity analysis by image warping

et al. 2014

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“…To reduce the computational cost of RTM, several multisource RTM methods have been proposed and developed [15] [16] [17] [18] [19] [20]. Unlike conventional RTM that deals with each shot separately, multi-source migration methods focus on forming a virtual source and the corresponding virtual shot gather by weighted summing multiple shots and their corresponding shot gathers simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-source RTM reduces the number of migrations significantly, thus improving computational efficiency. However, a serious drawback of these multi-source methods is that mismatched sources and shot gathers produce crosstalk artifacts under nonlinear imaging conditions [18], severely hurting the imaging quality. Various researches have been conducted to suppress the crosstalk by designing different coding functions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Enhancing Multisource Reverse Time Migration

Jia

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60°as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.

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“…), random shot‐encoded sources (Morton and Ober ; Romero et al . ) or any other phase‐/amplitude‐encoded source (Soubaras ; Perrone and Sava ) in the range of shot‐profile migration. Reverse‐time migration automatically and without additional costs outputs shot‐domain common‐image gathers; unfortunately, the migrated‐shot domain is notoriously not optimal for velocity estimation because of migration artefacts (Stolk and Symes ) and noisy common‐image gathers (Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

“…Reverse-time migration works only in common-shot and common-receiver configurations, so the choice of the shot domain for performing migration velocity analysis with a two-way engine may be preferable. Here, we use the word 'shot' in its broader sense to include synthetic shot-gathers like plane-wave sources (Whitmore, 1995;Zhang et al 2005;Liu et al 2006;Stoffa et al 2006), random shot-encoded sources (Morton and Ober 1998;Romero et al 2000) or any other phase-/amplitude-encoded source (Soubaras 2006;Perrone and Sava 2012a) in the range of shot-profile migration. Reverse-time migration automatically and without additional costs outputs shot-domain common-image gathers; unfortunately, the migrated-shot domain is notoriously not optimal for velocity estimation because of migration artefacts (Stolk and Symes 2004) and noisy common-image gathers (Zhang et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Wavefield tomography based on local image correlations

Perrone¹,

Sava

Panizzardi

2014

Geophysical Prospecting

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The estimation of a velocity model from seismic data is a crucial step for obtaining a high-quality image of the subsurface. Velocity estimation is usually formulated as an optimization problem where an objective function measures the mismatch between synthetic and recorded wavefields and its gradient is used to update the model. The objective function can be defined in the data-space (as in full-waveform inversion) or in the image space (as in migration velocity analysis). In general, the latter leads to smooth objective functions, which are monomodal in a wider basin about the global minimum compared to the objective functions defined in the dataspace. Nonetheless, migration velocity analysis requires construction of commonimage gathers at fixed spatial locations and subsampling of the image in order to assess the consistency between the trial velocity model and the observed data. We present an objective function that extracts the velocity error information directly in the image domain without analysing the information in common-image gathers. In order to include the full complexity of the wavefield in the velocity estimation algorithm, we consider a two-way (as opposed to one-way) wave operator, we do not linearize the imaging operator with respect to the model parameters (as in linearized wave-equation migration velocity analysis) and compute the gradient of the objective function using the adjoint-state method. We illustrate our methodology with a few synthetic examples and test it on a real 2D marine streamer data set.

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Wave‐equation migration with dithered plane waves

Cited by 10 publications

References 23 publications

Linearized wave-equation migration velocity analysis by image warping

Linearized wave-equation migration velocity analysis by image warping

Deep Learning for Enhancing Multisource Reverse Time Migration

Wavefield tomography based on local image correlations

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