Directional illumination analysis using the local exponential frame

Mao, Jian; Wu, Ru‐Shan; Gao, Jinghuai

doi:10.1190/1.3454361

Cited by 16 publications

(4 citation statements)

References 39 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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“…Illumination analysis in the framework of wave-equation migration is formulated using the solution to migration deconvolution problems (Berkhout, 1982;Yu and Schuster, 2003;Xie et al, 2006;Mao et al, 2010). Migration deconvolution first establishes a linear relationship between a reflectivity distributionr and seismic data d:…”

Section: Construction Of Illumination-based Penaltymentioning

confidence: 99%

Illumination compensation for image-domain wavefield tomography

2013

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Image-domain wavefield tomography is a velocity model building technique using seismic images as the input and seismic wavefields as the information carrier. However, the method suffers from the uneven illumination problem when it applies a penalty operator to highlighting image inaccuracies due to the velocity model error. The uneven illumination caused by complex geology such as salt or by incomplete data creates defocusing in common-image gathers even when the migration velocity model is correct. This additional defocusing violates the wavefield tomography assumption stating that the migrated images are perfectly focused in the case of the correct model. Therefore, defocusing rising from illumination mixes with defocusing rising from the model errors and degrades the model reconstruction. We addressed this problem by incorporating the illumination effects into the penalty operator such that only the defocusing by model errors was used for model construction. This was done by first characterizing the illumination defocusing in gathers by illumination analysis. Then an illumination-based penalty was constructed that does not penalize the illumination defocusing. This method improved the robustness and effectiveness of image-domain wavefield tomography applied in areas characterized by poor illumination. Our tests on synthetic examples demonstrated that velocity models were more accurately reconstructed by our method using the illumination compensation, leading to a more accurate model and better subsurface images than those in the conventional approach without illumination compensation.

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“…These methods can be summarized into two main categories. The first group applies the integral method, e.g., the local-plane wave decomposition and wavelet transform based methods [7][8][9], local Fourier transform based methods [10,11]. The second group applies differential methods, e.g., the Poynting vector or gradient vector-based methods [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

True Amplitude Angle Gathers from Reverse Time Migration by Wavefield Decomposition at Excitation Amplitude Time

Yan

Liu

2020

Energies

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Reservoir parameter estimation is one of the goals of amplitude-versus-angle (AVA) inversion and angle-domain common image gathers are the basis of AVA inversion. Therefore, the accuracy of kinematic and kinetic information on angle gathers is very important for reservoir characterization. Reverse time migration is one of the most physically accurate migration method. Generating angle gathers from reverse time migration with the Poynting vector method is very efficient. However, due to inaccurate angle measurement and uneven illumination, angle gathers calculated by the Poynting vector method are often not suitable for AVA inversion. In this paper, we propose an efficient method of angle gathers with accurate angular information and amplitude from reverse time migration. We firstly decompose source and receiver wavefield to their up-going and down-going parts by using analytic wavefield. We calculate propagation directions for source down-going wavefield and receiver up-going wavefield by the Poynting vector method and form the angle gathers with these angle information and decomposed wavefield. To reduce memory storage and improve computational efficiency, we decompose wavefield at excitation amplitude time by using a local spatial Fourier transform. We also use a spatial smoothed Poynting vector to improve the stability of angle measurement. We apply an illumination compensation image condition to recover the true amplitude. Numerical examples on Marmousi model and the SEAM two-dimensional (2D) model demonstrate the advantages of our proposed method. The angle gathers based on our method are cleaner with more focus on events energy and better continuity, suffering from less low-frequency noise in the shallow parts and with a distinct cutoff at large angle where reflection terminates. At last, we demonstrate the effectiveness of proposed method on a 2D marine field data example.

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Directional illumination analysis using the local exponential frame

Cited by 16 publications

References 39 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

Illumination compensation for image-domain wavefield tomography

True Amplitude Angle Gathers from Reverse Time Migration by Wavefield Decomposition at Excitation Amplitude Time

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