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学者姓名：高静怀
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Abstract ：
Amplitude-preserving angle gathers are ideal inputs for seismic prestack inversion. However, due to the limitation of computational efficiency, generating subsurface azimuth-reflection angle gathers from 3-D seismic imaging is still a very difficult task. In this article, we propose a new method to generate azimuth-reflection angle gathers from 3-D reverse time migration (RTM). The proposed method approximately reconstructs the source wavefield using high-dimensional wavelets and the excitation information. After using directional vectors to calculate the subsurface observation angles and applying the cross correlation imaging condition, we can generate azimuth-reflection angle gathers by angle binning. Without storing source wavefields or reconstructing source wavefields using boundary conditions, the proposed method has high computational efficiency. Numerical experiments on a synthetic model and a real marine seismic dataset demonstrate that compared with the excitation amplitude imaging condition, the proposed method can generate azimuth-reflection angle gathers with continuous complete events and high signal-to-noise ratio. The image quality and resolution of angle gathers are significantly improved. At the same time, the computational complexity does not increase much.
Keyword ：
Azimuth Azimuth-reflection angle gathers excitation amplitude imaging condition high-dimensional wavelets Oils reverse time migration (RTM) Wavelet domain
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| GB/T 7714 | Li, Feipeng , Gao, Jinghuai , Gao, Zhaoqi et al. Generating Azimuth-Reflection Angle Gathers From Reverse Time Migration Using the High-Dimensional Local Phase Space Approximation of Seismic Wavefields [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Li, Feipeng et al. "Generating Azimuth-Reflection Angle Gathers From Reverse Time Migration Using the High-Dimensional Local Phase Space Approximation of Seismic Wavefields" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Li, Feipeng , Gao, Jinghuai , Gao, Zhaoqi , Li, Chuang , Wang, Qingzhen , Sun, Wenbo et al. Generating Azimuth-Reflection Angle Gathers From Reverse Time Migration Using the High-Dimensional Local Phase Space Approximation of Seismic Wavefields . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
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Abstract ：
Least-squares reverse time migration (LSRTM) is an amplitude-preserving seismic imaging technique that aims at finding the subsurface reflectivity model. It is often performed iteratively using an inversion algorithm, such as the conjugate gradient method. Such an implementation requires a huge amount of calculation as it may converge slowly. Preconditioning plays a crucial role in seismic inverse problems. In this study, we propose a novel preconditioning method for LSRTM. The proposed method estimates a new guided curvelet-domain deblurring filter for one-step LSRTM and preconditioned LSRTM. Then, the filter is applied to migrated images and gradients in LSRTM. Such a deblurring filter acts as a curvelet-domain local linear approximation of the least-squares functional inverse Hessian, which can improve the image quality and accelerate the convergence. Numerical tests on the synthetic model and a field data example demonstrate that the preconditioning operator can effectively accelerate the convergence of LSRTM. One-step LSRTM can obtain comparable image quality to that of conventional iterative LSRTM with only a single iteration. The comparison of the convergence curves demonstrates that the curvelet-domain preconditioning operators accelerate the convergence of LSRTM. Furthermore, the preconditioned LSRTM achieves better image quality than the conventional LSRTM. We compare preconditioning operators based on diagonal and local linear approximations. The preconditioning operator based on the local linear approximation has more robust performance and more stable convergence curves than the diagonal-based approximation.
Keyword ：
Computational modeling Computed tomography Convergence Curvelet transform (CT) Data models deblurring filter Hessian Imaging least-squares imaging Mathematical models Perturbation methods reverse time migration
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| GB/T 7714 | Li, Feipeng , Gao, Jinghuai , Gao, Zhaoqi et al. Least-Squares Reverse Time Migration With Curvelet-Domain Preconditioning Operators [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Li, Feipeng et al. "Least-Squares Reverse Time Migration With Curvelet-Domain Preconditioning Operators" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Li, Feipeng , Gao, Jinghuai , Gao, Zhaoqi , Li, Chuang , Zhang, Wei . Least-Squares Reverse Time Migration With Curvelet-Domain Preconditioning Operators . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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Abstract ：
Data-driven deep-learning full-waveform inversion (DD-DLFWI) can efficiently reconstruct a velocity image of the subsurface from prestack seismic recordings, once the deep-learning (DL) model is well-trained based on the self-designed geological structures and simulated recordings. However, the key problem of this approach is that it usually discards the knowledge about the forward and adjoint operators, which leads to poor reconstruction quality and generalization ability. To mitigate these problems, we have developed a deep-learning full-waveform inversion (DLFWI) approach using seismic migration images. This approach includes two key points. The first key point is that, unlike the conventional DD-DLFWI approach based on the seismic recordings in the common-source data domain, our approach utilizes the reverse time migration (RTM) images of seismic recordings in the common-source image domain as the data engine of the convolutional neural network (CNN) to reconstruct the background velocity. The second key point is that we utilize the iterative neural network architecture to reconstruct the high-resolution velocity model based on the reconstructed background velocity. Specifically, the high-resolution velocity model can be recovered by using the reconstructed velocity model, RTM image, and gradient of regularization term as the input of neural network architecture. Through synthetic experiments with various layered and fault velocity models, we have confirmed that the proposed approach can reconstruct a high-resolution velocity of the subsurface from prestack seismic recordings. Meanwhile, it outperforms the conventional DD-DLFWI approach in terms of reconstruction accuracy, antinoise, and generalization ability.
Keyword ：
Data models Deep-learning Electronics packaging full-waveform inversion Image reconstruction inverse problem Inverse problems Iterative methods Neural networks reverse time migration Tools
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| GB/T 7714 | Zhang, Wei , Gao, Jinghuai . Deep-Learning Full-Waveform Inversion Using Seismic Migration Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Zhang, Wei et al. "Deep-Learning Full-Waveform Inversion Using Seismic Migration Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Zhang, Wei , Gao, Jinghuai . Deep-Learning Full-Waveform Inversion Using Seismic Migration Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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Abstract ：
Prestack least-squares reverse time migration based on a correlative objective function denoted as PCLSRTM can retrieve a higher quality image of the subsurface than standard least-squares reverse time migration based on a waveform-matching objective function. However, to invert the optimal migration images of different shots via a gradient-based iteration scheme, PCLSRTM tends to require a huge computational overhead for a large number of reverse time migration (RTM) and reverse time de-migration operators. We introduce a convolutional neural network (CNN) framework to improve the computational efficiency in PCLSRTM. Specifically, the CNN architecture aims to reconstruct the optimal reflection image for a single-shot recording from the standard RTM image. Besides, the migration model can be used as an additional input of CNN architecture. Limited by the absence of true reflectivity of the subsurface as the label data in seismic imaging, we employ the standard PCLSRTM images of a small part of total shot recordings as the labels to train the constructed CNN model. There are two benefits for the well-trained CNN model. On the one hand, it can directly invert a comparable image quality with the PCLSRTM image at a low computational cost, when the migration model does not contain a strong lateral velocity variation. On the other hand, one can use the inverted image from the well-trained CNN model as the initial image of the PCLSRTM approach to accelerate convergence. Through synthetic experiments with the Marmousi-2 and SEG/EAGE salt models and marine field data, it can determine that our approach can build a reflection image with balanced amplitude and good continuity and merely requires about one-ninth to one-fourth of the computational costs of PCLSRTM.
Keyword ：
Deep-learning Reverse time migration Seismic inversion Seismic migration
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| GB/T 7714 | Zhang, Wei , Gao, Jinghuai , Chen, Yuanfeng et al. Deep-learning for accelerating prestack correlative least-squares reverse time migration [J]. | JOURNAL OF APPLIED GEOPHYSICS , 2022 , 200 . |
| MLA | Zhang, Wei et al. "Deep-learning for accelerating prestack correlative least-squares reverse time migration" . | JOURNAL OF APPLIED GEOPHYSICS 200 (2022) . |
| APA | Zhang, Wei , Gao, Jinghuai , Chen, Yuanfeng , Li, Zhen , Jiang, Xiudi , Zhu, Jianbing . Deep-learning for accelerating prestack correlative least-squares reverse time migration . | JOURNAL OF APPLIED GEOPHYSICS , 2022 , 200 . |
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Abstract ：
Least-squares reverse time migration (LSRTM) has the potential to retrieve a high-resolution subsurface image. However, the standard acoustic LSRTM approach may produce a blurred image, if directly applying it to attenuated seismic recordings. In this letter, we developed a novel 3-D Q-compensated image-domain LSRTM approach, denoted as Q-IDLSRTM. The Hessian matrix in the proposed approach is efficiently estimated from the point spread functions (PSFs) which are calculated by a combination of viscoacoustic Born modeling and reverse time migration (RTM) based on the generalized standard linear solid (GSLS) wave equation. The major advantage of the proposed image-domain inversion is that it is much faster than data-domain inversion. The L1 norm constraint and total variation (TV) regularization are used to produce a sparse solution and maintain the structural continuity of the inverted image. We determine the effectiveness of the proposed approach with a part of the 3-D overthrust model and the resulting images demonstrate the ability of our approach to image subsurface structures with enhanced resolution and balanced amplitude relative to the RTM image and inverted image from the acoustic image-domain LSRTM approach.
Keyword ：
Attenuation Attenuation compensation Computational modeling image-domain inversion Mathematical models point spread functions (PSFs) reverse time migration (RTM) Solid modeling Standards Three-dimensional displays TV
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| GB/T 7714 | Zhang, Wei , Gao, Jinghuai . 3-D Q-Compensated Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Zhang, Wei et al. "3-D Q-Compensated Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Zhang, Wei , Gao, Jinghuai . 3-D Q-Compensated Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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Abstract ：
Least-squares reverse time migration (LSRTM) can estimate high-quality reflectivity of subsurface medium from seismic data. However, the subsurface reflectivity depends on reflection angles, and its variations over reflection angles are extremely important because they can be used to estimate subsurface physical properties for seismic interpretation. We present a new formulation of the LSRTM method that can estimate reflection-angle-dependent reflectivity from seismic data. We derive a forward modeling operator which predicts the reflection data without calculating the reflection angles, and verify that it approximately equals to the reflection-angle-dependent wave-equation-based Kirchhoff modeling operator under the assumption that the velocity perturbation is small and the reflection angle is smaller than the critical angle. Based on the proposed modeling operator associated with the adjoint of the angle-dependent wave-equation-based Kirchhoff modeling operator, we reformulate LSRTM as an inverse problem to invert for reflection-angle-dependent reflectivity using a preconditioned conjugate gradient algorithm. The algorithm uses a low-rank filter as the preconditioner to attenuate migration artifacts. Imaging tests on synthetic and field seismic data are used to verify validity and superiority of the proposed method. The tests illustrate that the proposed method can produce the reflection-angle-dependent reflectivity with much higher signal-to-noise ratio (SNR), resolution and amplitude fidelity than reverse time migration. Compared with conventional LSRTM, it can produce more focused stacked image when the migration velocity contains errors. Moreover, conventional LSRTM only produces the angle-independent reflectivity, whereas the proposed method has the feasibility to produce the reflection-angle-dependent reflectivity.
Keyword ：
Angle-dependent reflectivity Data models Kirchhoff modeling linearized inversion Mathematical models Perturbation methods Predictive models Reflection Reflectivity reverse time migration (RTM) Scattering seismic imaging
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| GB/T 7714 | Li, Chuang , Zhang, Yikang , Gao, Zhaoqi et al. Least-Squares Reverse Time Migration for Reflection-Angle-Dependent Reflectivity [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Li, Chuang et al. "Least-Squares Reverse Time Migration for Reflection-Angle-Dependent Reflectivity" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Li, Chuang , Zhang, Yikang , Gao, Zhaoqi , Li, Feipeng , Li, Zhen , Gao, Jinghuai . Least-Squares Reverse Time Migration for Reflection-Angle-Dependent Reflectivity . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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Abstract ：
The enormous computational overheads and excessive storage requirements are two obstacles to the data-domain least-squares reverse time migration (RTM) approach for the application of large-scale 3-D seismic data. To alleviate this problem, we have developed an image-domain least-squares RTM (IDLSRTM) approach through point spread functions (PSFs) and excitation-amplitude (EA) imaging condition, denoted as EA-IDLSRTM. The key point is that the EA imaging condition, as a cost-effective and practical imaging condition, is used to reconstruct the RTM image and localized PSFs. There are two benefits to this combination. One is that the EA imaging condition can effectively reconstruct the RTM image and localized PSFs with less computational overhead and storage requirement, relative to the zero-lag cross correlation (CC) imaging condition. Another important benefit is that the redundant source wavelets in both the RTM and PSF images computed by the CC imaging condition can be removed by the EA imaging condition, prior to the image-domain inversion. As a result, the proposed approach can explicitly reduce the condition number of the Hessian matrix used in the conventional IDLSRTM approach, which will produce a less ill-conditioned inverse problem. In addition, we introduce an angle-dependent filter for the attenuation of low-wavenumber artifacts to accelerate the convergence. Several experiments with synthetic and field data demonstrate that the proposed EA-IDLSRTM approach can efficiently and effectively recover the high-resolution and high-fidelity reflectivity image. Meanwhile, EA-IDLSRTM can provide better imaging quality than the conventional IDLSRTM approach in the case of relatively smoothed velocity.
Keyword ：
Angle filtering Extrapolation Image reconstruction Imaging imaging condition least-squares migration Lighting Mathematical models point spread functions (PSFs) Receivers reverse time migration (RTM) Three-dimensional displays
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| GB/T 7714 | Zhang, Wei , Gao, Jinghuai . 2-D and 3-D Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions and Excitation-Amplitude Imaging Condition [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
| MLA | Zhang, Wei et al. "2-D and 3-D Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions and Excitation-Amplitude Imaging Condition" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60 (2022) . |
| APA | Zhang, Wei , Gao, Jinghuai . 2-D and 3-D Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions and Excitation-Amplitude Imaging Condition . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2022 , 60 . |
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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 ：
波动方程 孔隙度 渗透率 体平均定理 完备的Biot方程 致密油气
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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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Abstract ：
Angle-domain common-image gathers (ADCIGs) that can be used for migration velocity analysis and amplitude-versus-angle analysis are important for seismic exploration. However, because of the limited acquisition geometry and seismic frequency band, ADCIGs extracted by reverse time migration (RTM) suffer from illumination gaps, migration artifacts, and low resolution. We have developed a reflection angle-domain pseudoextended plane-wave least-squares RTM method for obtaining high-quality ADCIGs. We build the mapping relations between the ADCIGs and the plane-wave sections using an angle-domain pseudoextended Born modeling operator and an adjoint operator. based on which we formulate the extraction of ADCIGs as an inverse problem. The inverse problem is iteratively solved by a preconditioned stochastic conjugate-gradient method, allowing for reduction in computational cost by migrating only a subset instead of the whole data set and improving the image quality thanks to preconditioners. Numerical tests on synthetic and field data verify that our method can compensate for illumination gaps, suppress migration artifacts, and improve resolution of the ADCIGs and the stacked images. Therefore, compared to RTM, our method provides a more reliable input for migration velocity analysis and amplitude-versus-angle analysis. Moreover, it also provides much better stacked images for seismic interpretation.
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| GB/T 7714 | Li, Chuang , Gao, Zhaoqi , Gao, Jinghuai et al. Angle-domain common-image gathers from plane-wave least-squares reverse time migration [J]. | GEOPHYSICS , 2021 , 86 (5) : S311-S324 . |
| MLA | Li, Chuang et al. "Angle-domain common-image gathers from plane-wave least-squares reverse time migration" . | GEOPHYSICS 86 . 5 (2021) : S311-S324 . |
| APA | Li, Chuang , Gao, Zhaoqi , Gao, Jinghuai , Li, Feipeng , Yang, Tao . Angle-domain common-image gathers from plane-wave least-squares reverse time migration . | GEOPHYSICS , 2021 , 86 (5) , S311-S324 . |
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Abstract ：
Time-frequency (TF) analysis is an available tool to estimate seismic absorption qualitatively. The high TF concentration is a key factor for the seismic attenuation qualitative estimation. To obtain a more concentrated TF representation, we propose a sparse TF method based on sparse representation (SR) and sparse Group-Lasso (GL) penalty function. Based on the SR theory, TF representation can be regarded as an inverse problem, and thus, sparse GL penalty function can be added in this inverse problem to enhance the TF concentration. Sparse GL penalty function, including l(1) penalty and l(2,1) penalty, can provide group-wise and within-group sparsity for TF coefficients. Using the proposed sparse GL-based TF (GLTF) method, we develop a workflow to characterize seismic attenuation qualitatively. Finally, a synthetic data of viscoacoustic model and a 2-D field data are applied to test the validity and effectiveness of the proposed workflow for indicating the gas and oil reservoirs.
Keyword ：
Inverse problem seismic absorption qualitative indicator sparse Group-Lasso (GL) time-frequency (TF) analysis
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| GB/T 7714 | Yang, Yang , Gao, Jinghuai , Wang, Zhiguo et al. Seismic Absorption Qualitative Indicator via Sparse Group-Lasso-Based Time-Frequency Representation [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (9) : 1680-1684 . |
| MLA | Yang, Yang et al. "Seismic Absorption Qualitative Indicator via Sparse Group-Lasso-Based Time-Frequency Representation" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18 . 9 (2021) : 1680-1684 . |
| APA | Yang, Yang , Gao, Jinghuai , Wang, Zhiguo , Li, Zhen . Seismic Absorption Qualitative Indicator via Sparse Group-Lasso-Based Time-Frequency Representation . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2021 , 18 (9) , 1680-1684 . |
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