Radon transform is to use the speed difference between primary wave and multiple wave to focus the difference on different"points"or"lines"in Radon domain,so as to suppress multiple wave.However,th...Radon transform is to use the speed difference between primary wave and multiple wave to focus the difference on different"points"or"lines"in Radon domain,so as to suppress multiple wave.However,the limited migration aperture,discrete sampling,and AVO characteristics of seismic data all will weaken the focusing characteristics of Radon transform.In addition,the traditional Radon transform does not take into account the AVO characteristics of seismic data,and uses L1 Norm,the approximate form of L0 Norm,to improve the focusing characteristics of Radon domain,which requires a lot of computation.In this paper,we combine orthogonal polynomials with the parabolic Radon transform(PRT)and find that the AVO characteristics of seismic data can be fitted with orthogonal polynomial coefficients.This allows the problem to be transformed into the frequency domain by Fourier transform and introduces a new variable,lambda,combining frequency and curvature.Through overall sampling of lambda,the PRT operator only needs to be calculated once for each frequency,yielding higher computational efficiency.The sparse solution of PRT under the constraints of the smoothed L0 Norm(SL0)obtained by the steepest descent method and the gradient projection principle.Synthetic and real examples are given to demonstrate that the proposed method has This method has advantages in improving the Radon focusing characteristics than does the PRT based on L1 norm.展开更多
针对三轴磁通门传感器非正交、灵敏度不一致、零偏以及构成梯度仪的两个磁通门传感器位置不一致引起的测量误差问题,建立了误差模型;基于地磁矢量模值短时不变原理,采用线性化最小二乘算法进行一个磁通门传感器参数的辨识;基于校准后三...针对三轴磁通门传感器非正交、灵敏度不一致、零偏以及构成梯度仪的两个磁通门传感器位置不一致引起的测量误差问题,建立了误差模型;基于地磁矢量模值短时不变原理,采用线性化最小二乘算法进行一个磁通门传感器参数的辨识;基于校准后三分量差值F范数最小原理,采用多元线性回归的方法进行另一个磁通门传感器参数以及两个磁通门传感器相对位置关系参数的辨识。实验结果表明,该方法能够将两个磁通门中最大总场误差从1 194.4 n T降低到30.0 n T,将三分量梯度仪最大输出误差从529.1 n T降低到13.4 n T,有效地改善了梯度仪性能。展开更多
K2DPCA(Kernel-based 2D Principal Component Analysis)能够刻画图像的非线性特征,同时保留原始图像的二维数据结构和邻域信息,在人脸识别领域具有成功的运用,但其对异常值比较敏感。为克服此问题,将“角度”的概念引入非线性空间,基...K2DPCA(Kernel-based 2D Principal Component Analysis)能够刻画图像的非线性特征,同时保留原始图像的二维数据结构和邻域信息,在人脸识别领域具有成功的运用,但其对异常值比较敏感。为克服此问题,将“角度”的概念引入非线性空间,基于核方法提出Sin-K2DPCA,并采用F范数度量,将样本数据经非线性映射到高维空间后极小化相对重构误差。为进一步解决非线性的核矩阵规模较大、计算复杂度高的问题,利用Cholesky分解方法,计算大规模核矩阵K的低秩近似,提出了基于Cholesky分解的Chol+SinK2DPCA。实验结果表明,在ORL、Yale人脸数据库中,Chol+SinK2DPCA提高了识别率,并克服噪声的影响;在大规模数据集Extended YaleB中,Chol+SinK2DPCA有效解决了K2DPCA由于核矩阵规模过大而不能实现的问题。展开更多
基金funded by the National Natural Science Foundation of China(No.41774133)major national science and technology projects(No.2016ZX05024-003 and 2016ZX05026-002-002)the talent introduction project of China University of Petroleum(East China)(No.20180041)
文摘Radon transform is to use the speed difference between primary wave and multiple wave to focus the difference on different"points"or"lines"in Radon domain,so as to suppress multiple wave.However,the limited migration aperture,discrete sampling,and AVO characteristics of seismic data all will weaken the focusing characteristics of Radon transform.In addition,the traditional Radon transform does not take into account the AVO characteristics of seismic data,and uses L1 Norm,the approximate form of L0 Norm,to improve the focusing characteristics of Radon domain,which requires a lot of computation.In this paper,we combine orthogonal polynomials with the parabolic Radon transform(PRT)and find that the AVO characteristics of seismic data can be fitted with orthogonal polynomial coefficients.This allows the problem to be transformed into the frequency domain by Fourier transform and introduces a new variable,lambda,combining frequency and curvature.Through overall sampling of lambda,the PRT operator only needs to be calculated once for each frequency,yielding higher computational efficiency.The sparse solution of PRT under the constraints of the smoothed L0 Norm(SL0)obtained by the steepest descent method and the gradient projection principle.Synthetic and real examples are given to demonstrate that the proposed method has This method has advantages in improving the Radon focusing characteristics than does the PRT based on L1 norm.
文摘针对三轴磁通门传感器非正交、灵敏度不一致、零偏以及构成梯度仪的两个磁通门传感器位置不一致引起的测量误差问题,建立了误差模型;基于地磁矢量模值短时不变原理,采用线性化最小二乘算法进行一个磁通门传感器参数的辨识;基于校准后三分量差值F范数最小原理,采用多元线性回归的方法进行另一个磁通门传感器参数以及两个磁通门传感器相对位置关系参数的辨识。实验结果表明,该方法能够将两个磁通门中最大总场误差从1 194.4 n T降低到30.0 n T,将三分量梯度仪最大输出误差从529.1 n T降低到13.4 n T,有效地改善了梯度仪性能。
