The objective function of full waveform inversion is a strong nonlinear function,the inversion process is not unique,and it is easy to fall into local minimum.Firstly,in the process of wavefield reconstruction,the wav...The objective function of full waveform inversion is a strong nonlinear function,the inversion process is not unique,and it is easy to fall into local minimum.Firstly,in the process of wavefield reconstruction,the wave equation is introduced into the construction of objective function as a penalty term to broaden the search space of solution and reduce the risk of falling into local minimum.In addition,there is no need to calculate the adjoint wavefield in the inversion process,which can significantly improve the calculation efficiency;Secondly,considering that the total variation constraint can effectively reconstruct the discontinuous interface in the velocity model,this paper introduces the weak total variation constraint to avoid the excessive smooth estimation of the model under the strong total variation constraint.The disadvantage of this strategy is that it is highly dependent on the initial model.In view of this,this paper takes the long wavelength initial model obtained by first arrival traveltime tomography as a prior model constraint,and proposes a weak total variation constrained wavefield reconstruction inversion method based on first arrival traveltime tomography.Numerical experimental results show that the new method reduces the dependence on the initial model,the interface description is more accurate,the error is reduced,and the iterative convergence efficiency is significantly improved.展开更多
在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯...在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯度信息可以自适应地选择去噪的最佳模式来平滑噪声图像,既能够在保护边缘的条件下预先处理高斯噪声,同时可以克服全变差模型的"阶梯效应"。结果表明,新模型不仅能够有效去除噪声,强化边缘还有效地保证了边缘结构的细节信息。在峰值信号噪声比测试中,该模型较之于传统线性滤波法的信噪比提高了20 d B左右,均方差也大幅降低,更具理想性。展开更多
基金supported by National Key R&D Program of China under contract number 2019YFC0605503CThe Major projects of CNPC under contract number(ZD2019-183-003)+2 种基金the Major projects during the 14th Five-year Plan period under contract number 2021QNLM020001the National Outstanding Youth Science Foundation under contract number 41922028the Funds for Creative Research Groups of China under contract number 41821002.
文摘The objective function of full waveform inversion is a strong nonlinear function,the inversion process is not unique,and it is easy to fall into local minimum.Firstly,in the process of wavefield reconstruction,the wave equation is introduced into the construction of objective function as a penalty term to broaden the search space of solution and reduce the risk of falling into local minimum.In addition,there is no need to calculate the adjoint wavefield in the inversion process,which can significantly improve the calculation efficiency;Secondly,considering that the total variation constraint can effectively reconstruct the discontinuous interface in the velocity model,this paper introduces the weak total variation constraint to avoid the excessive smooth estimation of the model under the strong total variation constraint.The disadvantage of this strategy is that it is highly dependent on the initial model.In view of this,this paper takes the long wavelength initial model obtained by first arrival traveltime tomography as a prior model constraint,and proposes a weak total variation constrained wavefield reconstruction inversion method based on first arrival traveltime tomography.Numerical experimental results show that the new method reduces the dependence on the initial model,the interface description is more accurate,the error is reduced,and the iterative convergence efficiency is significantly improved.
文摘在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯度信息可以自适应地选择去噪的最佳模式来平滑噪声图像,既能够在保护边缘的条件下预先处理高斯噪声,同时可以克服全变差模型的"阶梯效应"。结果表明,新模型不仅能够有效去除噪声,强化边缘还有效地保证了边缘结构的细节信息。在峰值信号噪声比测试中,该模型较之于传统线性滤波法的信噪比提高了20 d B左右,均方差也大幅降低,更具理想性。
