Full-wave seismic illumination and resolution analyses: A Poynting-vector-based method

Yan, Zhe; Xie, Xiao‐Bi

doi:10.1190/geo2016-0003.1

Cited by 24 publications

(17 citation statements)

References 55 publications

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“…For demonstration of combining deep learning and illumination analysis, we employ a simple method (Equations 2.2-2.5) to calculate the illumination, which costs little extra computational time in migration. When the geological model is large and complicated, it is necessary to adopt high-resolution illumination analysis methods, e.g., the local directional approaches (Mao et al, 2010;Yan & Xie, 2016), which require a lot of calculation time. The deep learning method can build the nonlinear mapping between the model and its corresponding single shot illumination result, and therefore efficient illumination analysis can be realized.…”

Section: Illumination Analysis Based On Deep Learningmentioning

confidence: 99%

An efficient local imaging strategy based on illumination analysis with deep learning

Rong

Jia

2021

Front. Earth Sci.

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We propose a deep-learning-based illumination analysis and efficient local imaging method. Based on the wavefield forward modeling, seismic illumination can intuitively express the energy propagation of direct waves, reflected waves, and transmitted waves, while it requires high calculation costs. We use a series of convolution operations in deep learning to establish the nonlinear relationship between the model and the illuminations to realize single-shot illumination result of the model. Stacking the single shot illumination results obtained by the network prediction can further help determine the target area. For the target area, we use a deep learning method to obtain the low illumination area of the geological model. Each shot has contribution to the low illuminated area; single shot is selected based on the contribution of the shot being greater than the average illuminance, and the low illumination area is imaged by reverse time migration on the selected shot gather. The trained convolutional neural network can help us quickly obtain the single shot illumination result of the model, which is convenient to analyze the energy distribution of various areas of geological model, and do further imaging for target areas. Using part of the shot gathers to image a local area can recover the complex geological structure of the area and improve the efficiency of reverse time migration especially for 3D problems. This method has universal applicability and is suitable for local imaging of various complex models such as subsalt areas and deep regions.

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Section: Illumination Analysis Based On Deep Learningmentioning

confidence: 99%

An efficient local imaging strategy based on illumination analysis with deep learning

Rong

Jia

2021

Front. Earth Sci.

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“…The imaging near location x can be expressed as the convolution of the point spreading function (PSF) at the point with the model velocity perturbation. (Gelius, et al, 2002;Xie et al, 2005b；Lecomte, 2008; Cao ，2013 ； Chen and Xie，2015; Yan and Xie, 2016;He et al, 2016).…”

Section: Influence Of Velocity Model Errors Of Different Scales On Thmentioning

confidence: 99%

The effect of strong near-surface scattering on the quality of seismic imaging

Xie

Hongxiao

et al. 2017

SEG Technical Program Expanded Abstracts 2017

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In land seismic exploration, the scattering generated by small-scale heterogeneities in the shallow layer can cause strong noise in seismic data, resulting in serious problems in the depth image. By introducing random velocity models to simulate velocity fluctuations in the near-surface layer, and using the point spreading function to characterize the image quality, we examined how the surface scattering can affect the migration image. The results revealed that lacking of intermediate scaled velocity information in the shallow part of the migration velocity model can cause phase or travel time errors in the extrapolated source and receiver wavefields, making them fail to focus. This is the primary reason causing deteriorated image quality in regions with strong near-surface scattering. By investigating the effect of velocity fluctuations of different scales to wavefield focusing, we found velocity fluctuations of 200-500 m scale are vital to provide correct phase and travel times. If this part of the information can be obtained and built into the migration velocity, the subsurface image quality can be largely improved. Adopting the high-precision near-surface velocity model building technique, it is possible to create the near-surface migration velocity with the required accuracy. The current study can be the basis for further numerical investigations and the field experiment.

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“…From the viewpoint of illumination analysis, the LSM for high‐resolution imaging in complex areas is migration with illumination compensation. The compensation can be implemented by ray‐based migrations (Gelius et al ., 2002; Lecomte, 2008), one‐way wave migrations (Wu and Chen, 2006; Xie et al ., 2006) and RTMs (Yan et al ., 2014; Yan and Xie, 2016; Hu et al ., 2016a). The ray‐based method for PSF calculation has superiority in terms of computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

An illumination‐compensated Gaussian beam migration for enhancing subsalt imaging

Liu

Yan

Zhu

et al. 2021

Geophysical Prospecting

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Seismic images of Earth's subsurface are essential for oil and gas exploration. Gaussian beam migration is popular for seismic imaging owing to its flexibility and efficiency of implementation. However, the practical use of classic Gaussian beam migration is limited for complicated structures such as subsalt because of poor seismic illumination in those areas. We propose an illumination‐compensated Gaussian beam migration under the framework of least‐squares migration, enhancing the subsalt imaging effectively. A novel scheme based on the Born modelling and migration of Gaussian beam is developed to estimate the point spreading function, with which the illumination compensation can be efficiently implemented in the local wavenumber domain. As the aim of least‐squares migration, the proposed illumination‐compensated Gaussian beam migration produces true‐amplitude images in subsalt areas, which facilitates the seismic interpretation of subsalt structures. The total computational cost of the proposed method includes one Born modelling process and two conventional Gaussian beam migrations, and thus it is much more efficient than the classic least‐squares migration which requires multiple iterations. Numerical examples have demonstrated the effectiveness of the adaptive strategies, manifesting applicable potential in oil and gas exploration of subsalt targets.

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Full-wave seismic illumination and resolution analyses: A Poynting-vector-based method

Cited by 24 publications

References 55 publications

An efficient local imaging strategy based on illumination analysis with deep learning

An efficient local imaging strategy based on illumination analysis with deep learning

The effect of strong near-surface scattering on the quality of seismic imaging

An illumination‐compensated Gaussian beam migration for enhancing subsalt imaging

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