海上地震勘探中震源阵列的定位算法研究

为了得到海洋地球物理勘探作业中震源阵列位置定位问题的解决方案,文中对航行作业过程中的地震勘探船只拖带的震源阵列实时位置计算方法进行了研究。首先,介绍了基于激光定位系统和相对GPS定位系统的震源阵列定位方法。为了解决相对GPS定位系统输出的定位数据粗差剔除、时间同步等预处理问题,提出了一种基于滑动缓冲区的多项式拟合粗差剔除和数据同步处理算法,满足了粗差剔除要求。并利用该模型函数实现了震源阵列放炮时刻观测值的内插,进而实现了放炮时刻和定位时刻的数据同步。在此基础上,研究了震源阵列导航定位模型,提出了以单个枪体为对象的卡尔曼滤波模型来描述震源阵列的运动状态,以满足海洋地球物理勘探震源阵列定位的需要。     

带乘性噪声系统状态滤波的自适应算法

带乘性噪声系统由于其广泛的适用性,一直成为研究的热点。针对带乘性噪声系统状态最优估计的自适应算法进行研究,探讨在噪声服从平稳正态分布情况下,对未知动态噪声方差阵与观测噪声方差阵的辨识问题。在证明带乘性噪声系统新息在稳态时和线性系统新息有着相似稳定特性的前提下,通过对线性系统辨识方法的改进,完成对带乘性噪声系统噪声方差阵的辨识,并利用新息特性对该方法进行进一步改进,以提高辨识精度;最后通过仿真验证该方法的有效性。     

序统计滤波估计检测海洋测深异常数据

根据海洋水深数据处理的要求，构建了海洋水深数据的序统计滤波模型，并根据水深异常数据的特点，提出了离差法判定水深异常数据，在遵循“舍深取浅”这一基本海洋数据处理原则基础上，设计了海洋水深异常数据检测的序统计滤波方案。实例分析表明，该滤波方案能够有效地检测并剔除零水深、负水深、孤立突跳水深，保留连续（个数多于（含）3个的）异常水深，在序统计滤波异常数据检测的基础上剔除粗差，能大幅提高粗差检测的效率。     

基于波形重定技术的杭州湾平均海面高模型建立

利用AVISO提供的SARAL卫星杭州湾周年的WAVEFORM数据,通过对卫星波形分析,提出了一种新的波形重定方法,该方法在顾及波形物理机制基础上,根据各波形的特征进行重定。对重定前后的波形数据进行粗差剔除、共线平均,分析了重定后数据质量。通过交叉点平差,结合验潮站数据建立杭州湾平均海面高模型,所建模型与验潮站平均海面高差值的平均值为0.006m,标准差为0.038m。结果表明,利用本文提出的方法能显著提高卫星测高数据的精度与质量。     

带乘性噪声系统在加性噪声相关时的最优固定域平滑及反褶积算法

最优平滑及最优反褶积在石油地震勘探、通讯工程、语音处理等应用领域都具有十分重要的意义。以往的带乘性噪声系统的平滑及反褶积大都对系统模型的噪声特性有着较强的限制条件 ,要求动态噪声及观测噪声互相独立或只能在同时刻相关。该文给出了一种在动态噪声为有色噪声及动态噪声和量测噪声在有限时间段上相关的情形下带乘性噪声系统的固定域平滑及反褶积算法 ,该算法在线性最小方差意义下是最优的。通过仿真计算 ,说明了该算法的有效性。     

一种具有SA预报能力的GPS KALMAN滤波器

对GPS定位中的SA误差作了系统辨识。表明：SA误差可用时变AR（p）模型表述，AR（p）的时变参数可由RLS算法结合F判据建立的AR（p）模型辨识机获得。用该方法获得的模型对SA误差进行预报。在剔除野值后精度在10m之内。还提出了一种将以AR（p）模型表述的SA误差加入GPSKALMAN滤波器的方法，可得到一种具有SA预报能力的GPSKALMAN滤波器。     

非线性随机系统的参数辨识及动态预报

鉴于含有加性噪声的指数模型描述了一类重要的非线性随机系统。本文给出这类系统的参数递推辨识算法，克服了迭代算法不能在线运行、需反复矩阵求逆的不足。当系统时变时，还采用了虚拟噪声技术来补偿因参数时变引起的建模误差，从而改善动态预报器的性能。应用这种方法，对油田采油井、注水井的套管损坏情况进行了多步动态预报，其精度令人满意     

多通道带乘性噪声系统的最优固定域平滑算法

在以往的乘性噪声系统的观测模型中 ,由于假定各通道的乘性噪声是完全相同的 ,因此并不是真正的多通道系统。而本文则考虑各通道乘性噪声不同的情况 ,即真正意义上的多通道带乘性噪声系统。在褚东升等“噪声相关时多通道带乘性噪声系统最优滤波”的基础上 ,进一步给出了固定域平滑算法。该算法在线性最小方差意义下为最优的。通过仿真计算 ,说明了该算法的有效性     

利用CUBE算法剔除多波束测深粗差研究

针对国内基于不确定度信息处理多波束测深数据相对滞后现状,将CUBE算法应用于大批量多波束测深粗差剔除研究。通过与人工交互编辑结果对比,表明CUBE算法剔除粗差效率较高并能保证测深成果质量,具有较高的推广应用价值。该研究对进一步优化我国多波束测深粗差处理算法也具有重要意义。     

测绘卫星摄影测量平差的粗差探测方法研究

粗差探测是平差解算中的重要课题,对平差解算结果可靠性的提升有着重要作用。因此,通过对典型摄影测量粗差探测权函数的分析,提出了一种能基于粗差率自适应调整权函数系数的粗差探测算法。实验结果表明,自适应粗差探测方法在不同粗差条件下的卫星摄影测量平差中能有效剔除粗差,具有较好的稳定性。     

带有色乘性噪声随机系统的逆向最优反卷积算法

通过建立状态和乘性色噪声的逆向 Markov模型 ,推导出了带有色乘性噪声随机系统的逆向最优状态滤波器。基于该滤波器给出了节省存储空间的逆向固定区间最优反卷积算法。仿真实例证明了该反卷积算法的有效性     

Direction-of-Arrival Estimation in the Presence of Unknown Nonuniform Noise Fields

A new method for estimating directions-of-arrival (DOA) of multiple spatial narrowband signals in the presence of spatially nonuniform independent sensor noise with unknown covariance is presented. An estimate of the colored noise-covariance matrix is given first. The received data for parameter estimation is then prewhitened using the estimated noise covariance, hence, overcoming the highly biased estimates. Furthermore, the performance improvement of standard MUSIC method is confirmed by computer simulations.     

"Principal component inverse" algorithm for detection in thepresence of reverberation

Detection in the presence of reverberation is often difficult in active sonar, due to the reflection/diffusion/diffraction of the transmitted signal by the ocean surface, ground, and volume. A modelization of reverberation is often used to improve detection because classical algorithms are inefficient. A commonly used reverberation model is colored and nonstationary noise. This model leads to elaborate detection algorithms which normalize and whiten reverberation. In this paper, we focus on a more deterministic model which considers reverberation as a sum of echoes issued from the transmitted signal. The Principal Component Inverse (PCI) algorithm is used with this model to estimate and delete the reverberation echoes. A rank analysis of the observation matrix shows that PCI is efficient in this configuration under some conditions, such as when the transmitted signal is Frequency Modulated. Both methods are validated with real sonar surface reverberation noise. We show that whitening has poor performance when reverberation and target echo have the same properties, while PCI maintains the same performance whatever the reverberation characteristics. Further, we extend the algorithms to spatio-temporal data. We propose a new algorithm for PCI which allows better echo separation. This new method is shown to be more efficient on real spatio-temporal data     

Super-resolution processing for HF surface wave radar based onpre-whitened MUSIC

Owing to the decametric wavelength, the angular resolution of high-frequency surface wave radar (HFSWR) is usually coarse, especially when dimensions of antenna arrays are restricted such as in shipborne HFSWR applications. In this paper, the relative strength of atmospheric noise and sea clutter that will heavily degrade the capabilities of HFSWR in target detection and resolution are calculated, then a method for estimating the spatial covariance matrix of background noise is presented. By introducing a pre-whitening procedure in multiple signal classification (MUSIC), the resolution performance of MUSIC is enhanced in spatially colored noise environment. Results with data from the aircraft detecting experiment conducted by the Research Institute of Electronic Engineering of Harbin Institute of Technology in 1994 and simulated data of two targets show that pre-whitened MUSIC can provide a better resolution and accurate determination of target number. Furthermore, a post-processing method is proposed to eliminate the sidelobe of spatial spectrum arising from the estimation errors of a noise covariance matrix     

Spark平台下的海表面温度并行预测算法

面对海量的海表面温度数据,如何使用大数据处理平台和新的处理技术来实时处理、分析并预测海表面温度数据,是一个亟待解决的问题。本文基于现阶段的时间序列方法和专家意见,首先,将类比合成方法引入到海表面温度预测应用中;其次,基于Spark平台提出了一种改进的快速DTW算法SparkDTW;最后,为了充分利用通过时间序列挖掘得到的信息,将SparkDTW与SVM相结合,提出了SparkDTW+SVM混合模型,为海表面温度预测的应用研究提供了较好的理论基础和技术支持。实验结果表明,SparkDTW算法预测精度优于SVM,提高了海表面温度预测效率,验证了将类比合成方法应用在海表面温度预测的可行性;SparkDTW+SVM在精度方面要优于SparkDTW和SVM,表明SVM模型能充分利用时间序列挖掘的信息,验证了SparkDTW+SVM在海表面温度预测的有效性。     

基于SVM回归模型的SAR图像有效波高估测

A new method for estimating significant wave height(SWH) from advanced synthetic aperture radar(ASAR) wave mode data based on a support vector machine(SVM) regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts(ECMWF) is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization(PSO) algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.     

Resolution of Seamount Geoid Anomalies Achieved by the SARAL/AltiKa and Envisat RA2 Satellite Radar Altimeters

The resolution of seamount geoid anomalies by the SARAL/AltiKa Ka-band radar altimeter is compared with the Envisat RA2 Ku-band altimeter using cross-spectral analysis of exact-repeat profiles. Noise spectra show white noise floors at root-mean-square levels around 8 mm per root-Hz for AltiKa and 19 mm per root-Hz for RA2, and are colored at wavelengths longer than a few km, with a spectral hump similar to that seen in Jason-2 data. The AltiKa noise level is lower than the RA2 noise level by more than one would expect from the ratio of their pulse repetition frequencies. Large outliers are present in data from both altimeters, always of one sign (range too long), and show little correlation with rain or other error flags. Seamount anomaly signal to noise ratios are 30 to 10 dB for AltiKa and 3 to 8 dB less for RA2, decreasing as seamount size decreases. Seamounts as small as 1.35 km tall are resolved by both instruments, with significantly better performance by AltiKa due to its lower noise level. If AltiKa can fly a geodetic mission, it will find many presently unknown seamounts.     

基于支持向量机预测安达曼海南部内孤立波传播特性

内孤立波是发生在密度稳定层化海水中的一种特殊的海洋内波.预测内孤立波传播难度较大.本文提出了一种方法,利用美国麻省理工学院大气环流模型(MITgcm)的内孤立波模型计算了大量模拟数据,建立数据库.采用机器学习的方法,建立一个基于支持向量机(support vector machine,SVM)的安达曼海南部内孤立波传播...     

Updated Support Vector Machine for Seismic Liquefaction Evaluation Based on the Penetration Tests

Simplified techniques based on in situ testing methods are commonly used to predict liquefaction potential. Many of these simplified methods are based on finding the liquefaction boundary separating two categories (the occurrence or non-occurrence of liquefaction) through the analysis of liquefaction case histories. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all the independent variables, such as the seismic and soil properties, using conventional modeling techniques. Hence, in many of the conventional methods that have been proposed, simplified assumptions have been made. In this study, an updated support vector machine (SVM) based on particle swarm optimization (PSO) is used to evaluate liquefaction potential in two separate case studies. One case is based on standard penetration test (SPT) data and the other is based on cone penetration test (CPT) data. The SVM model effectively explores the relationship between the independent and dependent variables without any assumptions about the relationship between the various variables. This study serves to demonstrate that the SVM can “discover” the intrinsic relationship between the seismic and soil parameters and the liquefaction potential. Comparisons indicate that the SVM models perform far better than the conventional methods in predicting the occurrence or non-occurrence of liquefaction.     

基于融合GMM与SVM的船载非侵入式负荷识别方法

为保障安全的船载用电环境,实现合理的用电配置与管理,在船载电力布局中引入非侵入式电力负荷监测。提出了一种融合高斯混合模型(GMM)与支持向量机(SVM)的电器类型识别算法。该方法利用暂态事件检测所提取的有效负荷特征,建立具有较好统计分布能力的GMM模型和具有较好泛化能力的SVM模型。对两种算法的概率分布进行融合生成最终识别结果。实验结果表明,相对单独应用SVM模型,本文所用方法在准确率和稳定性方面均有一定程度的提升,且实现复杂度低,具有良好的实用价值。