基于逆算子估计的AVO反演方法研究

传统反演算法以优化算法为主,而基于逆算子估计的AVO反演算法则利用了直接求逆的思路.算法的关键在于寻找存在逆函数的子域,进而可以在子域内直接求逆,这种解决反问题的思路不同于一般的优化类算法所采用的直接搜索解的方式,具有更高的效率.AVO反演利用了振幅随着偏移距的变化特征,反演的精度受到地震资料质量的影响,通过加入L1范数约束以及合理的初始模型有助于提高反演的稳定性以及准确度.模型测算和实际应用表明,基于逆算子估计的AVO反演方法具有较高的精确程度和可靠性.

基于逆算子估计的高阶AVO非线性反演

Zoeppritz方程线性近似式已被广泛应用于AVO反演方法研究中,但在界面差异明显时线性近似式难以满足精度要求,而高阶非线性近似式有助于解决此类问题。鉴于大多数优化算法并不太适用于求解非线性反演问题,本文提出一种新的AVO非线性反演方法:利用逆算子估计算法求解AVO反演问题,其直接求逆的思路有别于传统优化类反演算法的搜索方式,多解性显著减弱;应用Zoeppritz方程高阶近似式建立反演目标函数,提高了界面两侧差异明显时线性近似精度,还兼顾了纵横波速度比对AVO反演的影响。模型试算得知反演结果与模型值相符;实际应用结果表明,三参数反演结果与地震、测井信息吻合较好。这套基于逆算子估计的AVO反演方法具有较强的稳定性和可靠性,运用高阶近似式及选取合适的纵横波速度比是确保反演结果具有高精度的关键。

次高斯复随机矩阵的算子范数估计

在傅里叶光学、量子力学等物理领域,研究对象可用复随机矩阵描述,而复随机矩阵的算子范数估计是研究这些物理问题的重要工具,故对次高斯复随机矩阵的算子范数估计进行了研究。首先将实随机变量的性质推广到复随机变量上,并在此基础上证明了复随机变量的Hoeffding不等式和Bernstein不等式,然后利用ε-网技术给出了次高斯复随机矩阵算子范数的一个双侧界估计,最后用次高斯复随机矩阵范数估计的数值模拟实验验证了结论的准确性。

Hilbert空间中具有正算子系数的亚纯单叶算子值函数(英文)

本文讨论了 Hilbert空间中具有正算子系数的亚纯单叶算子值函数集 Σ[α,β],得到了 f( z)∈Σ[α,β]的充要条件及算子系数估计 ,并表明在算术平均及凸线性组合下Σ[α,β]是闭的 .

高阶Schrdinger方程的加权估计

本文主要研究的是相函数为齐次椭圆多项式的自由高阶 Schrdinger 方程．通过相函数等值面的几何性质,得到了解算子的 Strichartz 加权估计和极大算子加权估计．

迭代时间重排同步压缩变换及其在机械故障诊断中的应用

机械设备常运行在复杂环境下,往往受到时变载荷、时变转速、瞬态冲击等非平稳工况的影响,导致故障时有发生。时频分析技术可以兼顾时间和频率两个变量,得到了广泛应用。然而传统时频分析方法在提高时频聚集性和减弱交叉项之间存在矛盾,为了实现复杂环境下机械设备的故障诊断,提出迭代时间重排同步压缩变换方法。在时间重排同步压缩变换的基础上构造新的群延时估计算子,然后只需进行一次重排操作即可获得更锐利的时频表示。通过仿真信号和滚动轴承加速寿命试验数据验证所提方法的有效性。

近年来复Hermite插值的研究

介绍了近年来复Hermite插值多项式的研究进展,提出了该研究领域尚未解决的若干问题.

分数阶不可压缩Navier-Stokes-Coriolis方程周期解的存在性

本文研究了带有旋转效应的分数阶Navier-Stokes方程在给定周期外力作用下周期mild解的存在唯一性,并且建立了Besov空间中分数阶热半群的线性估计。首先,给出符号及函数空间的定义。其次,采用分数阶热半群的Lp-Lq估计,分别对分数阶不可压缩Navier-Stokes-Coriolis方程的线性项及非线性项进行了估计。最后证明了给定一个具有周期ω的外力f,分数阶不可压缩Navier-Stokes-Coriolis方程周期mild解是唯一存在的,且周期也为ω。

一种有效的SAR图像相干斑噪声抑制方法

由于散射现象,SAR图像受到固有的乘性相干斑噪声的影响。文章提出了一种有效的基于平稳树状小波变换和贝叶斯估计的SAR图像噪声抑制方法。为了在抑噪过程中保持边缘,对原始图像采用比值边缘检测算子获取边缘信息。仿真结果表明,相对于一些经典的滤波方法和基于平稳小波变换方法,此方法具有更好的噪声抑制作用和边缘保持能力。

统计学习在假设稳定下推广误差的界

统计学习理论已经是近来的热点研究问题,而算子的推广性能是统计学习的一个十分重要的部分。本文指出算子的推广误差界可以归结为期望误差估计子的偏置和方差的问题,在这些假设稳定概念下,得到了估计算子的偏置和方差,并证明了"几乎处处"稳定算子的广义有界差分不等式,在一致稳定假设下得到推广误差的指数界。

New subspace algorithm for blind channel estimation in OFDM systems

In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.

改进多旋翼飞行器自主定位稠密光流算法

基于光流传感器的目标检测与自主定位算法能较好地解决无人飞行器室内自主飞行的难题。本文首先分析了传统全局H-S算法和局部L-K算法的机理性误差,然后提出了改进的光流算法。通过剔除影响估计运动的干扰数据来优化五置信点选取方式,改变能窗选择方式对置信点加权得到新的光流能量泛函,并修正拉普拉斯算子估计计算光流梯度,加快算法迭代速度。

基于改进迭代收缩阈值算法的微观3D重建方法

迭代收缩阈值算法(ISTA)求解离焦深度恢复动态优化问题时,采用固定迭代步长,导致算法收敛效率不佳,使得重建的微观3D形貌精度不高。为此,提出一种基于加速算子梯度估计和割线线性搜索的方法优化ISTA——FL-ISTA。首先,在每一次迭代中,由当前点和前一个点的线性组合构成加速算子重新进行梯度估计,更新迭代点;其次,为了改变迭代步长固定的限制,引入割线线性搜索,动态确定每次最优迭代步长;最后,将改进的迭代收缩阈值算法用于求解离焦深度恢复动态优化问题,加快算法的收敛速度、提高微观3D形貌重建的精度。在对标准500 nm尺度栅格的深度信息重建实验中,与ISTA、快速ISTA(FISTA)和单调快速ISTA(MFISTA)相比,FL-ISTA收敛速度均有所提升,重建的深度信息值下降了10个百分点,更接近标准500 nm栅格尺度;与ISTA相比,FL-ISTA重建的微观3D形貌均方差(MSE)和平均误差分别下降了18个百分点和40个百分点。实验结果表明,FL-ISTA有效提升了求解离焦深度恢复动态优化问题的收敛速度,提高了微观3D形貌重建的精度。

一种新的混合演化多目标优化算法

在KKT(Karush-Kuhn-Tucker)条件下,m维的连续多目标优化问题的Pareto解集在决策空间是一个(m-1)维的流形(manifold)。随着算法的迭代,当前种群将分布在流形的周围。为充分利用这一规则特性(regularity property)以解决具有复杂PS(Pareto set)的多目标优化问题,本文提出一种基于差分算子和分布估计算子的混合子代生成算法。首先,引入一个参数来指示当前种群的收敛程度,即当前种群解个体所构成的数据的协方差矩阵的前(m-1)个特征值的和与所有特征值的和的比,比值越大,收敛程度越高;进而,根据不同比值,自适应调节差分算子和分布估计算子生成新解的数量。将该算法在tec09系列测试函数上进行仿真实验,并与RM-MEDA、NSGA-II-DE两个算法进行对比,实验结果表明,RM-MEDA/DE算法优于与之比较的其他算法。

Quantum Fourier Transform and Phase Estimation in Qudit System

The quantum Fourier transform and quantum phase estimation are the key components for many quantum algorithms, such as order-finding, factoring, and etc. In this article, the general procedure of quantum Fourier transform and phase estimation are investigated for high dimensional case run in a qudit quantum computer, and the quantum circuits are They can be seen as subroutines in a main program given.

Modified Kantorovich Operators Providing a Better Error Estimation

Abstract： The approximation operator for every kind of the objective function is different in approximation theory. For Lebesgue function, we introduce a kind of modified Kantorovich operators, which preserve the test functions 1 and x2. This type of modification enables better error estimation on the interval[＋√3/3,＋∞] than the classic ones. Finally, a Voronovskaya-type theoremfor these operators is also obtained.

Estimates of Central Moments for One Kind of Exponential-Type Operators

In this paper,the explicit estimates of central moments for one kind of exponential-type operators are derived.The estimates play an essential role in studying the explicit approximation properties of this family of operators.Using the proposed method,the results of Ditzian and Totik in 1987,Guo and Qi in 2007,and Mahmudov in 2010 can be improved respectively.

Novel algorithm for geomagnetic navigation

To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation, the unscented particle filter （UPF） was introduced to navigation system. The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors. However, this navigation system could only provide the position information. To provide all the kinematics states estimation of aircraft, a novel autonomous navigation algorithm, named unscented particle and Kalman hybrid navigation algorithm （UPKHNA）, was proposed for geomagnetic navigation, The UPKHNA used the output of UPF and barometric altimeter as position measurement, and employed the Kahnan filter to estimate the kinematics states of aircraft. The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously, and the horizontal positioning performance is better than that only using the UPF.

Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage:A Case Study from Beijing Municipality,China

The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.

Estimation of Design Sea Ice Thickness with Maximum Entropy Distribution by Particle Swarm Optimization Method

The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.