基于分布估计算法对重力固体潮信号独立主成分分析及谐波提取

固体潮信号是地球自转在月球和太阳作用下产生的混合复杂信号.本文提出一个重力固体潮信号的正交分解模型,通过将重力固体潮信号谐波分量,分解在两个正交的方向上,以提取独立的谐波分量.为了实现这一分解,本文采用独立成分分析算法进行验证分析.独立成分分析主要用于提取信号中的独立成分,但一般的独立成分分析算法具有对初始值敏感,收敛较早熟等问题.本文利用分布估计算法的学习概率模型全局寻优,克服独立成分分析算法缺点.通过实验仿真分析,改进算法可将重力固体潮信号成功分离出以下独立的谐波状态:半日波信号、日波信号、长周期波信号.并且,这些分离出的独立成分与本文提出的重力固体潮信号正交分解模型一致,分别对应于模型中的正交谐波分量.     

基于第二代Curvelet变换的面波压制(英文)

针对面波能量强、频率低、视速度低等特点,本文提出了基于第二代Curvelet变换的多尺度、多方向分解的面波压制方法,充分利用Curvelet变换的多尺度、多方向功能,分离出含有面波的尺度和方向然后利用面波与有效波在Curvelet域基本不重叠的特性,进行信噪分离处理。这种方法在实际叠前地震数据的处理中,能够有效压制面波,同时较好地保护有效反射波,特别是反射波的低频分量。文中提出的基于第二代Curvelet变换的去面波方法是一种较好的保幅去噪方法。     

基于中分辨率遥感影像的居民区震害信息提取

针对中分辨率遥感影像建筑物震害信息弱以及变化检测法受非震害信息影响大等弱点, 本文建立了一种基于变化检测的居民区震害信息快速提取方法. 该方法利用主成分变换增强震害信息, 采用监督分类法提取似居民区, 并用灯光影像数据进一步对似居民区提取结果进行优化, 从而很好地消除了变化检测方法中非震害因素的影响. 在此基础上, 以2001年印度M_W7.6地震的极重灾区为研究区域, 利用震前、 震后Landsat卫星TM图像和震区灯光影像数据, 对本文算法进行了验证和分析. 结果表明, 在30—50 m中分辨率遥感影像上, 以建筑物为主的居民区震后图像变化最为显著的震害特征是反射率变大, 本文所建立的居民区震害信息提取方法在解决中分辨率遥感影像震害目标信息弱、 背景复杂等方面效果明显.      

基于经验模态分解的分数维地震随机噪声衰减方法

经验模态分解算法(EMD)是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量(IMF);在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果.     

利用多源地球物理图像融合方法识别物性异常——以航空电磁和航磁数据为例

对于同一地质模型,将不同地球物理数据分别进行反演,通常得到多个不同的反演结果,研究多源地球物理数据综合解释,有助于实现复杂地形环境下目标体的精确识别和定位.本文采用一种基于非下采样轮廓波变换(Non-Subsampled Contourlet Transform,简称NSCT)的多源地球物理信息融合方法,以美国阿拉斯加库克湾西南部的Iliamna火山地区的实测数据(公开数据)为例,通过对多源地球物理图像数据进行综合分析,实现了磁化率和电阻率模型的融合.首先,利用NSCT将不同地层的多源图像分解为低频子带图像和高频子带图像;然后,设计了基于加权平均和新度量参数(New Metric Parameter,简称NMP)的融合规则,并分别对低频子带图像和高频子带图像进行融合;最后利用NSCT反变换得到包含磁化率和电阻率信息的融合图像.将融合结果与先验资料进行对比,结果显示两者具有较高的一致性,表明这一方法有效可靠,为目标体精确识别和快速定位提供了一种新的解决思路.     

小波变换在TM热红外遥感信息解释中的应用

如何有效利用TM热红外遥感图像是遥感应用和研究人员共同关心的问题．由于空间分辨率不高,其应用与研究程度远不及其它波段获取的图像．小波变换具有信号多尺度分解能力,可以把遥感图像(二维信号)按照近似与细节进行多尺度分解．对TM(或ETM^ )图像进行K-L变换,将可见光及近红外波段信息集中到一个主分量图像上,而后对热红外图像和该主分量图像同时进行多尺度小波分解,使用前者的近似系数与后者的细节系数重建图像,由此得到的合成图像既保留了原热红外图像的基本灰度分布信息,同时分辨率也得到了提高．     

Contourlet变换在局部角度属性提取中的应用

为了直观展示地质的构造走向、暗示细小裂缝信息,便于地震资料解释,应用Contourlet变换和无抽样Contourlet变换提取地震剖面的局部角度属性,提出了基于Contourlet变换的局部角度属性提取方法.首先用Contourlet变换或无抽样Contourlet变换将地震剖面分解成为不同尺度下的方向子带,然后将不...     

改进的IHS变换和小波变换相结合的图像融合

在分析IHS变换时发现,IHS变换中亮度分量的计算是从红绿蓝三种颜色中平均提取三分之一作为总亮度,而人眼对绿色最敏感（占60％）、其次是红色（占30％）、再者是蓝色（占10％）,故本文对亮度分量的计算公式做了修改。在此基础上,提出了一种改进的IHS变换和小波变换相结合的图像融合方法。首先对多光谱图像进行改进的IHS变换,然后利用小波变换分别对1分量和全色高分辨率图像做小波分解,并对小波低频系数基于局部能量进行融合,小波高频系数基于局部方差进行融合,最后进行IHS逆变换,得到融合结果图像。结果表明,本文提出的方法在保持光谱源图像特性方面有优势,也较多地保留了源图像的空间细节信息,融合图像的扭曲程度小。     

基于希尔伯特-黄变换的多分量地震去噪方法研究及应用

针对多分量地震数据具有信噪比低、波场比较复杂等特点,同时为了解决有效波和面波在低频带具有相关性且频带有重叠的部分.本文基于希尔伯特—黄变换(Hilbert—Huang transform,HHT)提出一种多分量地震数据去噪方法.HHT的核心部分是固有模态分解(Empirical mode composition method,EMD),它将地震信号自适应分解为各个尺度的固有模态函数(Intrinsic mode function,IMF);然后根据有效波和噪声分布在不同的分辨尺度范围内进行数据重构;对于一些尺度分布比较宽的,进行更加严格的EMD分解,以及通过瞬时参数阈值大小进行处理以达到更好的去噪效果.本文所做的工作主要针对多波资料中面波的处理,利用面波跟有效波的频率尺度和瞬时属性差异,从仿真数据、模型和实际地震数据进行面波的压制实验,取得了比较满意的压制效果,提高了地震资料的分辨率.也表明了该方法在多分量地震去噪处理中的可行性.     

基于独立分量分析的多次波盲分离方法研究

独立分量分析是一种以高阶统计量为基础的信号处理方法,它以分离非高斯混合信号为目标,建立起各个分量的统计独立性判据.在传统方法中,多次波压制技术是基于二阶统计量,求得最优解得基本前提是一次波和多次波正交.本文将ICA应用于多次波问题,在对地震数据基本构成作了分析的基础上,建立起多次波盲分离的ICA模型,并对其假设条件和固有不确定问题进行了详细的研究分析,然后给出了基于负熵的快速ICA算法并加以改进,最后进行仿真实验.试验结果表明,该方法能有效地压制地震资料中的多次波信息,较好的恢复一次波信息.     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.     

利用独立分量分析进行火山灰云遥感检测

火山灰云不但引起全球气候和环境系统的重大变化,而且还会威胁航空安全。热红外遥感技术为检测火山灰云提供了新手段,但是遥感数据自身的冗余和波段相关性大大降低了火山灰云的检测精度。独立分量分析(Independent Component Analysis,ICA)能够实现遥感数据的去相关和消除冗余,在火山灰云检测中具有一定的潜力。通过探索火山灰云的物理、化学性质,文中以2010年4月19日冰岛艾雅法拉(Eyjafjallajokull)火山灰云MODIS图像为数据源,在对MODIS数据进行主成分分析处理的基础上,利用ICA进行火山灰云检测。结果表明:ICA能够较好地从MODIS图像中获取火山灰云信息,所得结果与美国地质调查局标准光谱数据库和火山灰云SO2浓度分布具有较好的一致性,取得了较好的检测效果。     

Volcanic ash cloud detection from MODIS image based on CPIWS method

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.     

基于带状混合矩阵ICA实现地震盲反褶积

基于对地震反褶积本质上是一个盲过程的认识，引入高阶统计学盲源分离技术——独立分量分析（ICA）实现地震盲反褶积.在无噪声假设条件下，利用地震记录时间延迟矩阵和地震子波带状褶积矩阵，将地震褶积模型转化为一般线性混合ICA模型，采用FastICA算法，将带状性质作为先验信息，实现所谓带状ICA算法（B\|ICA），得到个数与子波算子长度相等的多个估计反射系数序列和估计子波序列，最后利用褶积模型提供的附加信息从中优选出最佳的反射系数序列及相应的地震子波.模型数据和实际二维地震道数值算例表明：对于统计性反褶积，在不对反射系数作高斯白噪假设，不对子波作最小相位假设的所谓“全盲”条件下，基于ICA方法（反射系数非高斯分布，地震子波非最小相位）可以较好解决地震盲反褶积问题，是基于二阶统计特性的地震信号统计性反褶积方法的提升，具有可行性和应用前景.     

Time-domain hyperbolic Radon transform for separation of P-P and P-SV wavefields

A time-domain hyperbolic Radon transform based method for separating multicomponent seismic data into P-P and P-SV wavefields is presented. This wavefield separation method isolates P-P and P-SV wavefields in the Radon panel due to their differences in slowness, and an inverse transform of only part of the data leads to separated wavefields. A problem of hyperbolic Radon transform is that it works in the time domain entailing the inversion of large operators which is prohibitively time-consuming. By applying the conjugate gradient algorithm during the inversion of hyperbolic Radon transform, the computational cost can be kept reasonably low for practical application. Synthetic data examples prove that P-P and P-SV wavefield separation by hyperbolic Radon transform produces more accurate separated wavefields compared with separation by high-resolution parabolic Radon transform, and the feasibility of the proposed separation scheme is also verified by a real field data example.     

基于核主成分分析的时间域航空电磁去噪方法

时间域航空电磁数据往往在测量过程中受到天然和人文噪声的干扰.如果不能很好滤除这些电磁噪声,那么将会降低资料质量、影响反演的精度,甚至获得错误的解释结果.本文提出了一种基于核主成分分析的去噪方法,通过核主成分分析提取叠加后衰减曲线的主成分,然后使用能量占比方法分离反映地下介质的有效信号和噪声,最后使用反映地下介质的特定成分进行重构.本文所推荐的去噪方法不仅能剔除天然噪声,例如天电产生的尖脉冲或者振荡,而且能有效地抑制人文噪声.分别使用基于核主成分分析的去噪方法,以及AeroTEM软件的处理方法对同样的吊舱式时间域直升机航空电磁勘查系统实测数据进行处理,并比较其结果.处理结果表明：所推荐的去噪方法要优于AeroTEM软件.     

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission (2003–2016), and satellite radiometric sea surface temperature (SST) data (1982–2016) over the Atlantic and Pacific Oceans are used with the aim of demonstrating signal separations of the North Atlantic Oscillation (NAO) from the Atlantic Multi-decadal Oscillation (AMO), and the El Niño Southern Oscillation (ENSO) from the Pacific Decadal Oscillation (PDO). CICA results indicate that ENSO-related patterns can be extracted from the Gravity Recovery And Climate Experiment Terrestrial Water Storage (GRACE TWS) with an accuracy of 0.5–1 cm in terms of equivalent water height (EWH). The magnitude of errors in extracting NAO or AMO from SST data using the complex EOF (CEOF) approach reaches up to ~50% of the signal itself, while it is reduced to ~16% when applying CICA. Larger errors with magnitudes of ~100% and ~30% of the signal itself are found while separating ENSO from PDO using CEOF and CICA, respectively. We thus conclude that the CICA is more effective than CEOF in separating non-stationary patterns.     

Multi—scale expression of spatial activity anomalies of earthquakes and its indicative significance on the space and time attributes of strong earthquakes

The noise model based on a trous wavelet algorithm produces a multi-scale expression of image through the combination of wavelet transform and a testing model of statistical significance.This kind of expression not only gives the formation and location of image structure on different scales,but also eliminates the influence of noise Since the algorithm does not need any priori hypotheses,it is suitable for the data with complex structure.The research line is employed in this paper to analyze the spatial activity of earthquake.The method of how to recognize and describe the multi-scale space activity of earthquake is emphatically discussed in this paper.Taking typical sequences in Southwest China as research cases,we systematically study the structure characters of spatial activity of earthquake on different scales.Results show that multi-scale space structure to some extent possesses indicative effect on strong epicenters.And the foreshock anomalies of Songpan seismic sequence also reveal interesting pattem during the spatial-temporal evolvement.     

Contourlet based seismic reflection data non-local noise suppression

In this paper, we propose a non-local, transform domain noise suppression framework to improve the quality of seismic reflection data. The original non-local means (NLM) algorithm measures similarities in the data domain and we generalize it in the nonsubsampled contourlet transform (NSCT) domain. NSCT gives a multiscale, multiresolution and anisotropy representation of the noisy input. The redundancy information in NSCT subbands can be utilized to enhance the structures in the original seismic data. Like the wavelet transform, NSCT coefficients in each subband follow the generalized Gaussian distribution and the parameters can be estimated using appropriate techniques. These parameters are used to construct our proposed NSCT domain filtering algorithm. Applications for synthetic and real seismic data of the proposed algorithm demonstrate its effectiveness on seismic data random noise suppression.