基于GPS-RTK技术的波浪能发电装置结构动态位移监测及滤波方法

利用全球定位系统实时差分技术（Global Positioning System Real-Time Kinematic,GPS-RTK）对波浪能发电装置进行结构变形监测得到的信号会存在噪声。针对这个问题,本文提出一种结合巴特沃斯（Butterworth）滤波器和自相关函数经验模态分解（Empirical Mode Decomposition,EMD）的滤波降噪算法,利用动态位移仿真信号进行了实验验证。结果表明：该滤波算法能够有效降低装置动态位移监测数据中噪声的影响,利用滤波算法对仿真信号进行降噪处理之后,可以提取出信号中包含的结构固有频率。     

地壳形变分析的抗差最小二乘配置迭代解法

利用最小二乘配置进行地壳形变分析,其结果的合理性关键在于经验协方差函数的拟合.考虑到观测数据存在粗差的情况,提出基于观测值中位数初值的抗差最小二乘配置方法和基于中位参数法的抗差最小二乘配置方法.两种方法首先分别利用观测值中位数给出观测值初始权阵以及利用中位参数法给出最小二乘配置初始解,然后均在给定协方差函数参数初始值的情况下,应用合适的等价权进行抗差估计并通过迭代计算,最终获得稳健的协方差函数参数估值及最小二乘配置解.利用本文提出的两种方法以及传统方法分别对庐山地震的GPS垂直位移数据和意大利L'Aquila地震的InSAR同震位移数据进行处理分析.结果表明:相对传统方法,基于观测值中位数初值的抗差最小二乘配置方法效果更好,更具稳健性.     

Application of Ultra-shallow Seismic Survey to the Wanggezhuang Fault in Qingdao

At present, there is less theoretical research and practical experience in the aspect of ultra-shallow seismic exploration to the target layers at depths of only tens of meters both at home and abroad. Seismic exploration plays an important role in the location of faults and active structures, but the depth dozens of meters below the ground surface is the blind area of any kind of deep and shallow seismic exploration. Starting from the point of view of detecting urban active faults, and using related theories and methods of geology, geophysics and mathematics, the paper discusses the preconditions for acquiring efficient ultra-shallow seismic survey results in complicated geological backgrounds in Qingdao. Taking the Qingdao area as an example in this paper, we study the depth condition of Quaternary deposits, and apply 4-8 stacking folds to satisfy the requirement to get the exploration results with high-resolution and high-SNR. Preliminary results reveal that selecting a proper surveillance layout is one of the keys to acquire authentic exploration results in ultra-shallow P-wave reflection exploration. Our results also show that ultra-shallow seismic reflection method in detecting faults in the Qingdao area has good application prospects.     

Depth of closure: New calculation method based on sediment data

Obtaining depth of closure (DoC) in an accurate manner is a fundamental issue for coastal engineering, since good results for coastal structures and beach nourishment depend mainly on DoC. Currently, there are two methods for obtaining the DoC, mathematical formulations and profile surveys. However, these methods can incur important errors if one does not take into account the characteristics and morphology of the area, or if one does not have a sufficiently long time series. In this work the DoC is obtained from the break in the trend of the sediment with the depth, that is, in general with the increase of the depth a decrease in the size of the sediment takes place. However, at one point this tendency changes and the size increases, and then decreases again. When comparing the point where the minimum sediment size occurs before the increase, it is observed that the error incurred is small compared to other methods. If the Standard Deviation of Depth Change (SDDC) method is considered as the most accurate method, the error incurred by the proposed method is less than 7%. In addition, it can be seen that the dispersion of the sediment method always occurs outside the zone of bar movement. Whereas in the methods of profiles survey (using 2 cm precision profiles), sometimes the DoC is obtained within the active zone of bar movement. In addition, where the relative minimum of the median sediment size is found, and the sizes of 0.063 and 0.125 mm predominate in the composition of the sample. Therefore, this new method allows the precise location of the DoC to be obtained in a fast and simple way. Furthermore, this method has the advantage that it is not affected by the modifications that may be experienced by both the study area and the cross-shore beach profile.     

风浪背景下舰船水压场信号的自适应AR模型预测滤波

为了有效地从风浪背景中检测舰船水压信号,首先根据舰船水压信号和波浪噪声的特点以时间序列的AR模型理论为依据,导出一种便于实现的自适应滤波器结构,并用NFTF自适应算法实现对舰船水压信号的预测滤波,取得了明显的效果.计算机仿真实验结果表明,给出的方法对抗波浪干扰具有良好的性能.     

VRS动态随机模型建模方法的综合比较

阐述了4种传统虚拟参考站(virtual reference station,VRS)随机模型建模方法及VENUS随机模型建模方法的作业原理,从验后单位权方差、模糊度精度因子ADOP、F-ratio、滤波残差等多个角度综合分析了各方法作业水平的有效性及优劣程度。研究表明,传统的标准随机模型建模方法、信噪比建模方法及高度角建模方法在VRS中的应用有效性最弱,传统的自适应随机模型方法和VENUS建模方法在不同层面体现出各自的优势,有互相借鉴的科研价值。     

中国大地测量数据处理60年重要进展 第二部分:大地测量参数估计理论与方法的主要进展

大地测量参数估计原理是大地测量数据处理的重要基础。中国学者经过60年不懈努力,在自由网平差、拟稳平差、半参数估计、不适定问题解算方法、抗差估计、自适应滤波等领域做出了重要贡献,建立了许多新的理论、模型和方法,推进了大地测量数据处理理论的进展。这一部分重点介绍建国60年来中国学者在大地测量数据处理原理、方法方面取得的成就。     

基于卫星加速度恢复地球重力场的去相关滤波法

基于加速度法恢复地球重力场时,卫星加速度是由卫星轨道数值微分得到,而数值微分会放大高频误差,进而降低了重力场解算结果的精度.针对数值微分导出的加速度误差具有有色噪声的特性,提出利用去相关算法构造白化滤波器对加速度有色噪声进行滤波处理,并根据去相关的基本原理分别构造了基于三点差分和ARMA模型的白化滤波器.采用不同噪声背景的CHAMP卫星模拟轨道数据进行解算,结果表明:基于去相关滤波解算的重力场模型精度均要比等权解算的重力场模型精度高,初步验证了去相关滤波方法的有效性.     

一种卫星重力梯度数据的ARMA最优滤波模型构建方法

由于卫星重力梯度观测的有色噪声特性和海量观测特征,在利用直接法进行重力场模型的最小二乘求解时,观测值的协方差阵为超大型的非对角阵,这给数值求解带来了极大困难.本文提出了一种基于先验误差功率谱密度的最优ARMA滤波模型构建方法,结合法方程的分块求解策略,可实现对卫星重力梯度观测值的高效滤波处理.数值仿真结果表明,利用最优ARMA滤波器进行时域滤波后,法方程的态性得到了明显改善,重力梯度观测值中的有色噪声得到了有效的"白化"处理,大地水准面精度得到了显著提升.     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.