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< Page ,Total 39 >
Optimization-inspired deep learning high-resolution inversion for seismic data EI SCIE
期刊论文 | 2021 , 86 (3) , R265-R276 | GEOPHYSICS
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Abstract :

Seismic high-resolution processing plays a critical role in reservoir target detection. As one of the most common approaches, regularization can achieve a high-resolution inversion result. However, the performance of regularization depends on the settings of the associated parameters and constraint functions. Further, it is difficult to solve an objective function with complex constraints, and it requires designing an optimization algorithm. In addition, existing algorithms have high computational complexity, which impedes the inversion of the large data volume. To address these problems, an optimization-inspired deep learning inversion solver is proposed to solve the blind high -resolution inverse (BHRI) problems of various seismic wavelets rapidly, called BHRI-Net. The method builds on ideas from classic regularization theory and recent advances in deep learning, and it makes full use of prior information encoded in the forward operator and noise model to learn an accurate mapping relationship. It unrolls the alternating iterative BHRI algorithm into a deep neural network, and it applies the convolutional neural network to learn proximal mappings, in which all parameters of the BHRI algorithm are learned from training data. Further, the proposed network can be split into two parts and incorporate the transfer learning strategy to invert field data, which increases the flexibility of the proposed network and reduces training time. Finally, the tests on synthetic and field data show that the proposed method can effectively invert the high-resolution data and seismic wavelet from observation data with improved accuracy and high computational efficiency.

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GB/T 7714 Chen, Hongling , Gao, Jinghuai , Jiang, Xiudi et al. Optimization-inspired deep learning high-resolution inversion for seismic data [J]. | GEOPHYSICS , 2021 , 86 (3) : R265-R276 .
MLA Chen, Hongling et al. "Optimization-inspired deep learning high-resolution inversion for seismic data" . | GEOPHYSICS 86 . 3 (2021) : R265-R276 .
APA Chen, Hongling , Gao, Jinghuai , Jiang, Xiudi , Gao, Zhaoqi , Zhang, Wei . Optimization-inspired deep learning high-resolution inversion for seismic data . | GEOPHYSICS , 2021 , 86 (3) , R265-R276 .
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An Adaptive Time-Varying Seismic Super-Resolution Inversion Based on L-p Regularization EI SCIE
期刊论文 | 2021 , 18 (8) , 1481-1485 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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Abstract :

The time varying seismic super-resolution inversion technique becomes more and more attractive in seismic exploration. However, mast existing inversion methods suffer from amplitude loss and manual adjustment parameters. In this letter, we present an adaptive time-varying seismic super-resolution inversion method based on the L-p(0 < p < 1) regularization to address these issues. First, the L-p-norm with 0 < p < 1 is applied to constrain the reflectivity to obtain a sparser and more robust solution than the L-1 regularization. To solve the nonconvex inversion problem adaptively, second, we provide a new algorithm called singular value decomposition (SVD)-Hadamard product parametrization (HPP). The idea of the new algorithm is to apply an HPP to express the L-p(0 < p <= 1) regularization into a sum of the L-2 regularizations that are easy to be programed and solved. Then, the SVD is adopted to solve each L-2 regularization. It is convenient to apply the L-curve method or its variants to determine the regularization parameters at each iteration for finishing the inversion adaptively. Finally, synthetic and field data examples are tested to validate the effectiveness of the proposed method.

Keyword :

Reflectivity inversion time-varying super-resolution inversion

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GB/T 7714 Chen, Hongling , Gao, Jinghuai , Zhang, Bing . An Adaptive Time-Varying Seismic Super-Resolution Inversion Based on L-p Regularization [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (8) : 1481-1485 .
MLA Chen, Hongling et al. "An Adaptive Time-Varying Seismic Super-Resolution Inversion Based on L-p Regularization" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18 . 8 (2021) : 1481-1485 .
APA Chen, Hongling , Gao, Jinghuai , Zhang, Bing . An Adaptive Time-Varying Seismic Super-Resolution Inversion Based on L-p Regularization . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (8) , 1481-1485 .
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Construction of Optimal Basic Wavelet via AIDNN and Its Application in Seismic Data Analysis EI SCIE
期刊论文 | 2021 , 18 (7) , 1144-1148 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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Continuous wavelet transform (CWT) is an effective tool for seismic time-frequency (TF) analysis. Selecting a matched wavelet to the analyzed seismic wavelet is a key issue for accurately characterizing TF features of seismic data. The three-parameter wavelet (TPW) can match different seismic wavelets by adjusting the three parameters. However, it is difficult to select appropriate parameters for matching TPW to seismic wavelets in real applications. In this letter, we propose a basic wavelet construction method by using the TPW and the deep learning network. The proposed workflow first builds a mapping relationship between seismic wavelet and seismic data by using the alternating iterative deep neural network (AIDNN). Based on this relationship, we then estimate a seismic wavelet. Using the estimated seismic wavelet, we can finally construct an analytical basic wavelet by matching the TPW to the extracted wavelet. Note that we named the TPW with optimal parameters as the optimal basic wavelet (OBW), and its wavelet transform is OBWT. To demonstrate the validity and effectiveness of the proposed approach, we apply it to synthetic traces and field data for characterizing their TF features. The results show that OBWT preserves the amplitude better and has a higher resolution than the CWT with mismatched basic wavelets to the seismic wavelet, which is helpful for seismic data analysis in the future.

Keyword :

Tools Wavelet analysis optimal basic wavelet (OBW) Continuous wavelet transforms Alternating iterative deep neural network (AIDNN) three-parameter wavelet (TPW) Data analysis Training

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GB/T 7714 Tian, Yajun , Gao, Jinghuai , Liu, Naihao et al. Construction of Optimal Basic Wavelet via AIDNN and Its Application in Seismic Data Analysis [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (7) : 1144-1148 .
MLA Tian, Yajun et al. "Construction of Optimal Basic Wavelet via AIDNN and Its Application in Seismic Data Analysis" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18 . 7 (2021) : 1144-1148 .
APA Tian, Yajun , Gao, Jinghuai , Liu, Naihao , Chen, Daoyu . Construction of Optimal Basic Wavelet via AIDNN and Its Application in Seismic Data Analysis . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (7) , 1144-1148 .
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A Sequential Iterative Deep Learning Seismic Blind High-Resolution Inversion EI SCIE
期刊论文 | 2021 , 14 , 7817-7829 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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Abstract :

Seismic blind high-resolution inversion (BHRI) aims at retrieving the high-resolution data to characterize the stratigraphic structures in the case of an unknown seismic wavelet. However, the unknown wavelet and ill-posedness pose a great challenge to the high-resolution inversion. Regularization-based BHRI is an effective approach. However, it is sensitive to the sets of initial values, regularization terms, and regularization parameters and suffers from computational burden problems. To address these issues, we propose a sequential iterative deep learning method (SIDLM) to implement a BHRI in a fast computational speed, which incorporates three learned components to sequentially invert initial high-resolution data, seismic wavelet, and final high-resolution data in an end-to-end fashion. Specifically, to mitigate the influence of initial values, a data-driven network U-Net is adopted to learn an initial high-resolution data. Furthermore, the architecture makes use of prior information encoded in the forward operator to build a new general alternating direction method of multipliers (ADMM)-like iterative deep neural network, instead of the traditional alternating iterative inversion. The proposed ADMM-like network utilizes the convolutional neural networks to learn the proximal operators to solve each subproblems in alternating iterative inversion. Therefore, all parameters of BHRI, such as the regularization parameters and transform operator, can be implicitly learned from the training datasets in an end-to-end fashion, not limited to the form of the penalty function. Finally, the synthetic and field data examples are conducted to demonstrate the effectiveness of the proposed SIDLM.

Keyword :

Deep learning Wavelet domain iterative deep learning Convex functions Wavelet transforms Training data seismic inversion high-resolution inversion Alternating direction method of multipliers (ADMM) Data models Iterative algorithms

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GB/T 7714 Chen, Hongling , Gao, Jinghuai , Gao, Zhaoqi et al. A Sequential Iterative Deep Learning Seismic Blind High-Resolution Inversion [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2021 , 14 : 7817-7829 .
MLA Chen, Hongling et al. "A Sequential Iterative Deep Learning Seismic Blind High-Resolution Inversion" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14 (2021) : 7817-7829 .
APA Chen, Hongling , Gao, Jinghuai , Gao, Zhaoqi , Chen, Daoyu , Yang, Tao . A Sequential Iterative Deep Learning Seismic Blind High-Resolution Inversion . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2021 , 14 , 7817-7829 .
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Building large-scale density model via a deep-learning-based data-driven method EI SCIE
期刊论文 | 2021 , 86 (1) , M1-M15 | GEOPHYSICS
WoS CC Cited Count: 1
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Abstract :

As a rock-physics parameter, density plays a crucial role in lithology interpretation, reservoir evaluation, and description. However, density can hardly be directly inverted from seismic data, especially for large-scale structures; thus, additional information is needed to build such a large-scale model. Usually, well log data can be used to build a large-scale density model through extrapolation; however, this approach can only work well for simple cases and it loses effectiveness when the medium is laterally heterogeneous. We have adopted a deep-learning-based method to build a large-scale density model based on seismic and well log data. The long short-term memory network is used to learn the relation between seismic data and large-scale density. Except for the data pairs directly obtained from well logs, many velocity and density models randomly generated based on the statistical distributions of well logs are also used to generate several pairs of seismic data and the corresponding large-scale density. This can greatly enlarge the size and diversity of the training data set and consequently leads to a significant improvement of the proposed method in dealing with a heterogeneous medium even though only a few well logs are available. Our method is applied to synthetic and field data examples to verify its performance and compare it with the well extrapolation method, and the results clearly display that the proposed method can work well even though only a few well logs are available. Especially in the field data example, the built large-scale density model of the proposed method is improved by 11.9666 dB and 0.6740, respectively, in peak signal-tonoise ratio and structural similarity compared with that of the well extrapolation method.

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GB/T 7714 Gao, Zhaoqi , Li, Chuang , Zhang, Bing et al. Building large-scale density model via a deep-learning-based data-driven method [J]. | GEOPHYSICS , 2021 , 86 (1) : M1-M15 .
MLA Gao, Zhaoqi et al. "Building large-scale density model via a deep-learning-based data-driven method" . | GEOPHYSICS 86 . 1 (2021) : M1-M15 .
APA Gao, Zhaoqi , Li, Chuang , Zhang, Bing , Jiang, Xiudi , Pan, Zhibin , Gao, Jinghuai et al. Building large-scale density model via a deep-learning-based data-driven method . | GEOPHYSICS , 2021 , 86 (1) , M1-M15 .
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Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion EI
期刊论文 | 2021 , 87 (1) | Geophysics
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Seismic acoustic-impedance (AI) inversion, which estimates the AI of the reservoir from seismic and other geophysical data, is a type of nonlinear inverse problem that faces the local minima issue during optimization. Without requiring an accurate initial model, global optimization methods have the ability to jump out of local minima and search for the optimal global solution. However, the low-efficiency nature of global optimization methods hinders their practical applications, especially in large-scale AI inversion problems (AI inversion with a large number of traces). We propose a new intelligent seismic AI inversion method based on global optimization and deep learning. In this method, global optimization is used to generate datasets for training a deep learning network and it is used to first accelerate and then surrogate global optimization. In other words, for large-scale seismic AI inversion, global optimization only inverts the AI model for a few traces, and the AI models of most traces are obtained by deep learning. The deep learning architecture that we used to map from seismic trace to its corresponding AI model is established based on U-Net. Because the time-consuming global optimization inversion procedure can be avoided for most traces, this method has a significant advantage over conventional global optimization methods in efficiency. To verify the effectiveness of the proposed method, we compare its performance with the conventional global optimization method on 3D synthetic and field data examples. Compared with the conventional method, the proposed method only needs about one-tenth of the computation time to build AI models with better accuracy. © 2022 Society of Exploration Geophysicists.

Keyword :

Acoustic impedance Efficiency Deep learning Inverse problems Global optimization Seismology

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GB/T 7714 Gao, Zhaoqi , Yang, Wei , Tian, Yajun et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion [J]. | Geophysics , 2021 , 87 (1) .
MLA Gao, Zhaoqi et al. "Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion" . | Geophysics 87 . 1 (2021) .
APA Gao, Zhaoqi , Yang, Wei , Tian, Yajun , Li, Chuang , Jiang, Xiudi , Gao, Jinghuai et al. Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion . | Geophysics , 2021 , 87 (1) .
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致密油气介质中波的控制方程
期刊论文 | 2021 , 51 (3) , 353-363 | 中国科学(地球科学)
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Abstract :

致密油气介质是一种特殊的多孔介质,在油气勘探开发中占有重要地位.文章建立致密油气介质中波的控制方程,该方程较一般多孔介质波方程,形式大为简化,可用于由地震数据进行物性参数反演.文章首先简单介绍从孔隙尺度上流体的运动方程和固体骨架颗粒运动方程出发,利用体平均定理推导出完备的Biot方程的思路与结果,厘清了其中使用的假设条件.其次以岩石物理测试结果为基础,详细分析了致密油气介质中渗透率的时间变化率的性质,进而利用Kozeny-Carman方程,研究了孔隙度的时间变化率的性质,提出了致密油气介质中孔隙度作为状态变量的一个合理假设.在此基础上,从完备的Biot方程出发,推导出了致密油气介质中波的控制方程.该方程与经典的“弥散黏滞方程”在形式上一致,通过对比,得到了弥散黏滞方程中的系数和有明确物理意义的介质物性参数间的解析关系式.文中通过数值模拟验证了所建立的方程的正确性.基于所建立的方程,研究了单一致密夹层的地震波反射和透射特性.数值模拟结果表明夹层的厚度和衰减特性对于地震波的反射和透射有显著影响,这一认识对油气探测有重要意义.

Keyword :

致密油气 渗透率 孔隙度 完备的Biot方程 体平均定理 波动方程

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GB/T 7714 高静怀 , Weimin HAN , 何彦斌 et al. 致密油气介质中波的控制方程 [J]. | 中国科学(地球科学) , 2021 , 51 (3) : 353-363 .
MLA 高静怀 et al. "致密油气介质中波的控制方程" . | 中国科学(地球科学) 51 . 3 (2021) : 353-363 .
APA 高静怀 , Weimin HAN , 何彦斌 , 赵海霞 , 李辉 , 张懿洁 et al. 致密油气介质中波的控制方程 . | 中国科学(地球科学) , 2021 , 51 (3) , 353-363 .
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Hidden physics model for parameter estimation of elastic wave equations EI SCIE
期刊论文 | 2021 , 381 | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
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A numerical approach based on the hidden physics model to estimate the model parameters of elastic wave equations with the sparse and noisy data is presented in this paper. Through discretizing the time derivatives of elastic wave equations and placing the priors of the state variables as Gaussian process, the model parameters and structure of elastic wave equations are encoded in the kernel function of a multi-output Gaussian process. In the learning stage, a parameter bound constraint condition is incorporated to enforce the physical bound of the model parameters. The numerical results from several benchmark problems, including homogeneous media, layer media, anisotropic media, and homogeneous model with an inclusion, demonstrate the feasibility and performance of the hidden physics model. (C) 2021 Elsevier B.V. All rights reserved.

Keyword :

Sparse measurements Parameter bound-constraint Parameter estimation Gaussian Process regression Physics-constrained

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GB/T 7714 Zhang, Yijie , Zhu, Xueyu , Gao, Jinghuai . Hidden physics model for parameter estimation of elastic wave equations [J]. | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING , 2021 , 381 .
MLA Zhang, Yijie et al. "Hidden physics model for parameter estimation of elastic wave equations" . | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 381 (2021) .
APA Zhang, Yijie , Zhu, Xueyu , Gao, Jinghuai . Hidden physics model for parameter estimation of elastic wave equations . | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING , 2021 , 381 .
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Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion EI SCIE
期刊论文 | 2021 , 59 (10) , 8913-8932 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
WoS CC Cited Count: 1
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Seismic full-waveform inversion (FWI) aims to build high-resolution images of the physical properties of the subsurface. However, the ill-posedness and nonlinear problems pose a great challenge to the high-resolution reconstruction. Although the nonlinear problem can be mitigated by matching a subset of observation data, the resulting images are generally low-resolution background structures. Regularization-based techniques can mitigate the ill-posedness of FWI, but the iterative method suffers from the cycle-skipping and computational burden problems. To overcome these problems, we develop an adjoint-driven deep-learning FWI (AD-DLFWI) approach which utilizes the fully convolutional network (FCN) to invert subsurface velocity from reflection seismic data. Specifically, AD-DLFWI is implemented in a two-step iterative scheme, in which an optimal update result at each step is learned via a FCN-based learned updating operator. The proposed approach uses the seismic image of applying the adjoint operator of the scattering wave equation, which is equivalent to the gradient of classical FWI, as the data engine of FCN. Inspired by the wave-equation migration velocity analysis approach, we propose to unfold the gradient of FWI into the common-source domain to keep the information about the measure of velocity error. To ensure the interpretability of each network's role, we design a two-step training scheme to successively reconstruct the low and high wavenumber components of subsurface velocity. Using synthetic experiments with reflection-dominant seismic data, we have confirmed that the proposed FWI approach not only can provide a reliable velocity estimation but also is not sensitive to the cycle-skipping problem.

Keyword :

fully convolutional network Image reconstruction deep-learning Adjoint-driven Mathematical model inverse problem Linear programming Training full-waveform inversion Inverse problems Data models Reliability

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GB/T 7714 Zhang, Wei , Gao, Jinghuai , Gao, Zhaoqi et al. Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2021 , 59 (10) : 8913-8932 .
MLA Zhang, Wei et al. "Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59 . 10 (2021) : 8913-8932 .
APA Zhang, Wei , Gao, Jinghuai , Gao, Zhaoqi , Chen, Hongling . Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2021 , 59 (10) , 8913-8932 .
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Least-squares reverse time migration using convolutional neural networks EI
期刊论文 | 2021 , 86 (6) | Geophysics
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Least-squares reverse time migration (LSRTM) has the potential to reconstruct a high-resolution image of subsurface reflectivity. However, the current data-domain LSRTM approach, which iteratively updates the subsurface reflectivity by minimizing the data residuals, is a computationally expensive task. To alleviate this problem and improve imaging quality, we develop a LSRTM approach using convolutional neural networks (CNNs), which is referred to as CNN-LSRTM. Specifically, the LSRTM problem can be implemented via a gradient-like iterative scheme, in which the updating component in each iteration is learned via a CNN model. In order to make the most of observation data and migration velocity model at hand, we utilize the common-source RTM image, the stacked RTM image, and the migration velocity model rather than only the stacked RTM image as the input data of CNN. We have successfully trained the constructed CNN model on the training data sets with a total of 5000 randomly layered and fault models. Based on the well-trained CNN model, we have proved that the proposed approach can efficiently recover the high-resolution reflection image for the layered, fault, and overthrust models. Through a marine field data experiment, it can determine the benefit of our constructed CNN model in terms of computational efficiency. In addition, we analyze the influence of input data of the constructed CNN model on the reconstruction quality of the reflection image. © 2021 Society of Exploration Geophysicists.

Keyword :

Input output programs Reflection Seismic prospecting Image reconstruction Computational efficiency Convolutional neural networks Convolution

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GB/T 7714 Zhang, Wei , Gao, Jinghuai , Yang, Tao et al. Least-squares reverse time migration using convolutional neural networks [J]. | Geophysics , 2021 , 86 (6) .
MLA Zhang, Wei et al. "Least-squares reverse time migration using convolutional neural networks" . | Geophysics 86 . 6 (2021) .
APA Zhang, Wei , Gao, Jinghuai , Yang, Tao , Jiang, Xiudi , Sun, Wenbo . Least-squares reverse time migration using convolutional neural networks . | Geophysics , 2021 , 86 (6) .
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