基于稀疏反演的高效混采数据分离方法

混叠采集技术能极大地提高地震采集效率，但是由于连续激发，混采数据中存在严重的邻炮干扰噪声，而且混采数据规模巨大，这要求后续的信噪分离算法精确、稳定、快速，为此提出一种基于稀疏反演的高效混采数据分离方法。在三维共检波点道集上，首先通过三维快速傅里叶变换，将时空域数据变换到频率-波数-波数域（FKK）；然后采用硬阈值函数提取相干信号，利用相干信号预测混叠噪声；最后以数据残差为驱动，不断收缩阈值，迭代更新模型，实现高效混采数据分离。模拟数据和实际数据应用结果表明，本文方法能够精确、稳定、快速地分离高混叠度的地震数据。     

面向多源数据融合的稀疏表示目标跟踪

传统的基于稀疏表示的目标跟踪方法主要利用目标的灰度特征构建稀疏表示模型。由于灰度特征对光照变化敏感，这会影响目标跟踪在复杂场景下的鲁棒性。基于多源数据融合的目标跟踪可以明显提升目标跟踪鲁棒性，但如何有效融合不同维度，不同类型的多源目标特征成为基于多源数据融合的目标跟踪所要解决的关键问题。提出了一个基于目标状态以及灰度特征的稀疏表示目标跟踪方法。所提出的方法可通过基于核函数表示的稀疏表示模型，在探究目标状态以及灰度特征相关性的基础上，将两种不同维度的特征进行有效融合，提升目标跟踪在复杂场景下的鲁棒性。     

一种改进的最小二乘回归子空间分割方法

最小二乘回归(LSR)算法是一种常见的子空间分割方法，由于LSR具有解析解，因此它的聚类性能较高。然而LSR算法是应用谱聚类方法聚类数据，谱聚类方法初始化聚类中心是随机的，会影响后面的聚类效果。针对这一问题，提出一种基于聚类中心局部密度和距离这2个特点的改进的LSR算法（LSR-DC）。在Extended Yale B数据集上进行实验，结果表明，该算法有较高的聚类精度，具有一定的鲁棒性，优于现有LSR等子空间分割方法。     

CEEMD与KSVD字典训练相结合的去噪方法

本文提出一种完备总体经验模态分解（CEEMD）方法与K奇异值分解（KSVD）学习字典算法相结合的地震信号去噪方法。含噪信号通过CEEMD分解得到一系列不同尺度的固有模态函数（IMF）；按频率由高到低依次排列IMF各分量，并做自相关分析，去除噪声主导的IMF分量；将累加的过渡IMF分量叠加重构并做CEEMD二次分解，通过自相关分析再次去除噪声主导的IMF分量；分别叠加二次CEEMD分解剩余的IMF分量和一次剩余的IMF分量，得到两个新的含噪信号，并利用KSVD过完备字典分别稀疏表示该两个新的含噪信号，即由稀疏系数重构去噪后地震信号，进而重构最终去噪结果。实验结果证明：该算法的去噪效果明显优于F-X去噪、小波阈值去噪和KSVD字典稀疏去噪等传统方法。     

基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断

利用层次化分块正交匹配算法（HBW-OOMP）的高稀疏性和运算速度快等优点，提出了一种基于K-奇异值分解（K-SVD）字典和HBW-OOMP算法的故障轴承诊断方法。首先利用K-SVD自学习训练方法得到包含冲击成分的冗余字典，克服了固定结构字典适应性不强的缺点。然后采用基于分块思想的HBW-OOMP算法进行原子的选取和稀疏系数的求解，以重构信号包络谱峭度最大为终止条件，自适应确定分解次数。最后应用所提方法对仿真信号和故障轴承实验信号进行故障特征提取，结果表明该方法能够有效提取强背景噪声下故障特征成分，具有一定的应用前景。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

基于稀疏表示的增维叠前地震反演方法

常规的叠前和叠后反演技术难以满足刻画薄储层的需求。由于道集叠加而产生的干涉效应对地震信号所蕴含的真实信息造成不同程度的弱化、畸变甚至湮灭，因此为有效地保存真实信息，应尽量避免叠加。另外，叠前道集的地震属性在随炮检距变化之外还受频率因素的影响，而以高分辨率谱分解为基础展开的FAVO反演方法能够提升流体识别能力。为此，以不叠加为核心思想，提出基于稀疏表示的增维叠前地震反演方法。首先，从叠前道集出发，对包含目的层段的角度内道集进行筛选和提取，依托信号的稀疏表示理论对抽选的单角度地震数据开展高分辨率时频分解；其次，基于贝叶斯理论直接建立波阻抗与地震数据的映射关系，利用非线性最优化算法对初始模型进行扰动；最后，以前一个频率反演的结果作为后一个频率反演的约束，逐级进行反演，最终得到不同角度的高分辨率增维反演结果。以A研究区为例，在有效角度范围内对单角度地震数据分频约束反演，拓宽了数据维度，提升了反演精度，增强了对薄储层的描述能力。