一维双相介质孔隙率的小波多尺度反演

基于多尺度的思想,将小波多分辨分析和多尺度方法结合，构造了小波多尺度反演方法，并应用于一维双相介质孔隙率的反演.利用小波变换，将原始反问题分解为不同尺度上的一系列子反问题，并按照尺度从粗到细的顺序依次求解.在每一个尺度上，都采用稳定、收敛快的正则化高斯牛顿法求解，次一级尺度上求出的“全局最优解”作为上一级的初始解，依次类推，直到求出原始问题的真正的全局最优解.将小波多尺度方法归结为三种不同算子(分解算子、求解算子、插入算子)的交替应用，给出了小波多尺度反演算法的基本流程图，并推导出当采用Daubechie     

探地雷达反问题的小波-同伦混合反演算法

构造一种求解探地雷达反问题的小波-同伦混合反演算法.利用小波将反问题分解到不同尺度上,在最大尺度上采用同伦反演方法,求得次级尺度的初始近似解,在其余尺度上进行多次迭代修正,以获得全局最小点.算法结合小波反演和同伦反演的优点,数值结果表明其具有较强的全局搜索能力.     

大尺度分层介质电特性参数的反演方法研究

针对大尺度分层介质电磁场散射回波数据, 提出了一种用于分层介质特性参数反演的新方法, 实现了分层介质的介电常数、电导率和介质厚度的同时反演. 方法首先将这些参数的反演转化为最优化问题进行求解, 然后应用模拟退火算法搜索最优解, 充分利用算法的全局寻优能力, 同时改善了算法的搜索控制策略, 使算法在最优解的搜索过程中可以自适应地调整搜索步长, 提高了算法的搜索效率. 结果表明, 本文方法可以实现大尺度分层介质特性参数的准确反演, 且具有较强的抗噪声能力. 该方法可以应用于火星/月球 雷达探测回波数据的反演以及地下分层结构特性的分析. 关键词： 电磁散射 分层介质 模拟退火算法 最优化     

薄膜椭偏测量的自适应混合反演算法

基于梯度的线性反演方法计算效率高,基于随机扰动的模拟退火方法寻找最优解能力强针对薄膜椭偏测量的多极值问题,综合两者的优点,提出一种求解薄膜椭偏测量问题的混合反演算法.模型每次扰动采用线性寻优方法搜寻局部最优解,叠代过程中采用均匀设计的模拟退火方法随机搜寻模型,使该算法有跳出局部最优解的能力,可以在较少的叠代次数内搜寻到全局最优解,从而提高求解薄膜椭偏测量非线性反演方法的计算效率.对反演过程控制参数进行讨论,该算法具有自适应的特点.计算表明,该算法可有效求解薄膜椭偏测量的多极值问题.     

用进化策略方法反演二维弹性波动方程的参数

从材料响应的理论合成与实际测量数据相拟合出发,将二维弹性波动方程的参数反演问题归结为非线性多峰函数的最优化问题.全局最优解的求解采用了进化策略法,并同遗传方法的反演结果进行了比较.数值结果表明,用进化策略方法进行参数反演的精度大大高于用遗传方法进行参数反演的精度,进化策略反演是一种良好的非线性反演方法.     

多层高反膜光学常数和厚度的反演

基于精英保留策略的遗传算法具有良好的全局寻优能力和很强的鲁棒性,基于Levenberg-Marquardt 算法的非线性最小二乘法具有快速、高效的局部收敛能力.研究了将这两种算法结合起来求解多层膜光学常数和厚度的可行性和有效性.结果表明:基于两种算法的组合反演算法综合了这两种算法的优点,能以较大的概率、较高的精度找到在实际约束条件下多层膜的光学常数和厚度,特别是在寻找全局最优解方面表现出色.根据全光谱透射拟合法基本原理,应用组合反演算法分别在考虑测量误差与不考虑测量误差两种情况下反演 15 层规整高反膜的光学常数和厚度,得到了较为精确的薄膜参数.模拟实验表明:此组合反演算法可有效地求解多层膜反演问题,在反演多层膜光学常数和厚度方面具有实际的应用价值.     

多波导阵列耦合研究

提出了一种求解波导阵列耦合特性的方法.采用拉普拉斯变换,将同时考虑相邻和次相邻波导耦合作用的波导阵列耦合模方程组变为一个线性方程组,利用矩阵的LU分解法(将系数矩阵分解为单位下三角阵L与上三角阵U的乘积)求出该线性方程组的解.然后根据给定的初始条件进行拉普拉斯变换反演,求出耦合模方程组的解.采用该方法研究了五波导阵列,光从中间一个波导入射和从五个波导入射时的耦合特性,给出了每个波导中光功率变化规律曲线,并将所得结果与同样条件下仅考虑相邻波导耦合的结果做了比较.采用该方法可以求出任意数目波导组成的阵列,考虑任意波导之间耦合的耦合模方程组的解.     

基于小波变换的多尺度图像融合增强算法

为解决原始单源图像缺乏多尺度细节信息和图像融合后出现的噪声问题,提出了一种基于小波变换的多尺度图像融合增强算法,根据不同频率子带分量采用不同融合规则的思想,对高频子带提出了三种融合方法,同时构建了一种新颖的多尺度残差金字塔空间并将其参与融合过程,以减少融合噪声.多种小波分解和对比实验结果表明,提出的小波多尺度融合增强算...     

WDM网络保护容量问题的遗传算法求解

本文以遗传算法为基础,设计了一个与传统数学方法完全不同的遗传搜索寻优算法并提出一种简捷解编码方式,该编码能够极大地简化选择、交叉和变异等遗传算子的执行.仿真实验结果表明文章算法能够迅速地求出保护容量优化问题的全局近似最优解,且能够满足工程设计的要求.     

混沌免疫多目标算法求解认知引擎参数优化问题

合理的认知引擎参数设置可以提高频谱的使用性能. 通过分析认知无线网络中的认知引擎参数配置, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌免疫多目标优化的求解方法. 算法使用Logistic混沌映射初始化种群, 并在每一代将混沌特性用于最优解集的搜索; 设计了适合此问题的免疫克隆算子和抗体群更新算子, 保证了Pateto最优解集分布的多样性和均匀性. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 算法可以根据信道条件和用户服务的动态变化, 自适应调整各个子载波的发射功率和调制方式, 可以求出更多满足偏好需求的解, 满足认知引擎参数优化要求.     

Wavelets-regularization method for particle size inversion in photon correlation spectroscopy

For ill-posed inversion problem of photon correlation spectroscopy (PCS), a wavelet-regularization inversion method (WRIM) which combines wavelet multiscale inversion strategy with classical regularization inversion method (CRIM) was proposed. By using this method, the original inversion problem is decomposed into several subproblems on different multiscale spaces. As a result, we can successively obtain solution of original inversion problem according to the particle sizes inverted from the coarsest scale to the finest scale. The simulation and experimental data was respectively inverted by two methods. The inversion results demonstrate that WRIM has better global convergence, higher accuracy and more strong noise immunity than CRIM.     

An efficient inversion algorithm for atmospheric remote sensing with application to UV limb observations

In this paper we present a retrieval algorithm for atmospheric remote sensing. The algorithm combines Tikhonov regularization and the iteratively regularized Gauss-Newton method and is devoted to the solution of multi-parameter inverse problems with simple bounds on the variables. The basic features of the algorithm: the solution of the bound-constrained minimization problem, the selection of the optimal regularization parameter, the derivation of the global regularization matrix and the characterization of the solution (error analysis) are discussed in detailed. The inversion algorithm is applied to ozone retrieval from SCIAMACHY limb scatter measurements in the ultraviolet spectral range.     

Ground Penetrating Radar Nondestructive Testing Based on Wavelet Sparse Representation

Now Ground penetrating radar (GPR) nondestructive testing methods have been applied to many fields of physics. But traditional electromagnetic methods (usually based on least square and local iteration) just roughly give the information of location, level and quality. In this paper we consider inverse electromagnetic problem which is concerned with the estimation of electric conductivity of Maxwell’s equations. A wavelet multilevel representation is proposed to inversion of GPR nondestructive testing. Once we decompose the objective functional onto different levels from the smallest to the largest, there are very few local minimum on the largest level component of the problem. Then local convergent Gauss-Newton method could easily find the global minimum on this level which is close to the global optimization solution on the second largest level. So, Gauss-Newton method with initial value which is solved on the largest level has serious possibility to find the global minimum of the second largest level. Repeating this step one could find the global optimization solution of the original inverse problem. On each level, the stable and fast local convergent Gauss-Newton method is carried out. Results exhibits clear advantages over damping Gauss-Newton method and testify that it is an available method, especially on aspects of wide convergence and precision.     

基于逆散射理论的地震波速度正则化反演

本文基于逆散射理论利用正则化有限差分对比源反演算法对地震波传播速度进行反演, 该方法是基于波动方程的频率域波形反演算法, 利用非线性共轭梯度法, 通过最小化目标优化函数不断迭代更新速度模型. 由于地球物理反演问题的病态性和不稳定性, 通过基于反演参数总变差的正则化处理, 使反演问题变为良性问题且算法具有较强的抗噪声干扰能力. 反演过程中使用了频率-空间域9点差分正演算子以及PML吸收边界条件. 与其他反演算法相比, 由于背景模型在反演迭代过程中保持不变, 可以避免在每次迭代过程中重新构造正演算子及矩阵分解等相关计算过程, 使得该算法非常适合于大规模三维反演计算. 此外, 本文采用基于MPI的并行计算, 进一步提高了反演计算的效率. 二维CSEG模型反演结果表明该方法可以反演得到高分辨率的地震波速度重建结果, 为地震勘探数据处理及解释提供准确的速度信息.     

阻尼型高斯-牛顿法及其在高频电磁波测井反演中的应用

提出一种改进的阻尼型高斯牛顿优化算法,通过引入阻尼矩阵,对反演参数依其相对修改量不同而给以不同的阻尼作用,并将它用于高频电磁波测井资料的反演中.     

Iterative regularization methods for atmospheric remote sensing

In this paper we present different inversion algorithms for nonlinear ill-posed problems arising in atmosphere remote sensing. The proposed methods are Landweber's method (LwM), the iteratively regularized Gauss-Newton method, and the conventional and regularizing Levenberg-Marquardt method. In addition, some accelerated LwMs and a technique for smoothing the Levenberg-Marquardt solution are proposed. The numerical performance of the methods is studied by means of simulations. Results are presented for an inverse problem in atmospheric remote sensing, i.e., temperature sounding with an airborne uplooking high-resolution far-infrared spectrometer.     

Optimized constraints for the linearized geoacoustic inverse problem

A geoacoustic inversion scheme to estimate the depth-dependent sound speed characteristics of the shallow-water waveguide is presented. The approach is based on the linearized perturbative technique developed by Rajan et al. [J. Acoust. Soc. Am. 82, 998-1017 (1987)]. This method is applied by assuming a background starting model for the environment that includes both the water column and the seabed. Typically, the water column properties are assumed to be known and held fixed in the inversion. Successful application of the perturbative inverse technique lies in handling issues of stability and uniqueness associated with solving a discrete ill-posed problem. Conventionally, such problems are regularized, a procedure which results in a smooth solution. Past applications of this inverse technique have been restricted to cases for which the water column sound speed profile was known and sound speed in the seabed could be approximated by a smooth profile. In this work, constraints that are better suited to specific aspects of the geoacoustic inverse problem are applied. These techniques expand on the original application of the perturbative inverse technique by including the water column sound speed profile in the solution and by allowing for discontinuities in the seabed sound speed profile.     

Adaptive multiscale finite-volume method for nonlinear multiphase transport in heterogeneous formations

In the previous multiscale finite-volume (MSFV) method, an efficient and accurate multiscale approach was proposed to solve the elliptic flow equation. The reconstructed fine-scale velocity field was then used to solve the nonlinear hyperbolic transport equation for the fine-scale saturations using an overlapping Schwarz scheme. A coarse-scale system for the transport equations was not derived because of the hyperbolic character of the governing equations and intricate nonlinear interactions between the saturation field and the underlying heterogeneous permeability distribution. In this paper, we describe a sequential implicit multiscale finite-volume framework for coupled flow and transport with general prolongation and restriction operations for both pressure and saturation, in which three adaptive prolongation operators for the saturation are used. In regions with rapid pressure and saturation changes, the original approach, with full reconstruction of the velocity field and overlapping Schwarz, is used to compute the saturations. In regions where the temporal changes in velocity or saturation can be represented by asymptotic linear approximations, two additional approximate prolongation operators are proposed. The efficiency and accuracy are evaluated for two-phase incompressible flow in two- and three-dimensional domains. The new adaptive algorithm is tested using various models with homogeneous and heterogeneous permeabilities. It is demonstrated that the multiscale results with the adaptive transport calculation are in excellent agreement with the fine-scale solutions. Furthermore, the adaptive multiscale scheme of flow and transport is much more computationally efficient compared with the previous MSFV method and conventional fine-scale reservoir simulation methods.