瞬时振幅、瞬时相位、瞬时频率在地质雷达工程中的应用

将探地雷达的实信号转换成复信号,分别提取瞬时振幅、瞬时相位、瞬时频率波形图,根据各自特征,综合分析,推断地下介质形状及性质.     

高分辨率地震信号瞬时特征分析方法

基于常规复数道分析方法计算地震信号瞬时特征参数,用于常规地震剖面解释中,其精度完全可以满足要求。但对高分辨率地震勘探,特别是对薄层的研究,其精度就不够了。在充分研究常规复数道分析算法及应用效果的基础上,提出了能够用于高分辨率资料解释的新复数道分析的方法。模型算例和实际资料处理结果表明,该方法可以有效地提高地震资料的分辨率。     

探地雷达复信号分析的几点讨论

采用H ilbert变换的复信号分析方法,求取相位时对噪声很敏感,所以分析前的滤波处理是非常重要的,常用的反正切求取相位角方法,不利于求取包含真实反射信息的相位θ(t)。针对以上两个问题,采用了与探地雷达垂向分辨能力相关的尺度,对原始数据进行了滤波操作。滤波后的数据保证了与原始数据相同的分辨能力,有效地压制了干扰信号。同时,具有良好的数学性质,为复信号分析提供了有利的条件。利用反余弦计算相角,方便了程序设计,且求得的相角易于变换为真实相角(ω0t θ(t)),为提取真正与反射信息相关的相位θ(t)提供了便利。应用优化方法求解瞬时相位、瞬时频率等信息,将真正与反射信息相关的θ(t)与ω0t分离,提高了分辨能力,同时还可得到ω0随时间变化的曲线,为分析探地雷达的频散现象提供了依据。     

基于Hilbert-Huang变换的探地雷达信号增强及复信号分析

阐述了Hilbert-Huang变换原理,着重讨论了EMD分解应满足的条件及具体分解过程.以黑麋峰抽水蓄能电站进厂交通隧洞实测GPR剖面为例,应用EMD分解去除部分噪声,再利用Hilbert变换求取GPR剖面复信号,提取瞬时振幅、瞬时相位、瞬时频率3个参数,绘制出相互独立的瞬时参数剖面图,根据不同地质状况进行多参数综合解释.结果表明,该方法避免了使用单一时距剖面分析所造成的解释偏差,能够更好地对异常信息做出反映,提高了GPR数据的解析精度.     

瞬时属性估计器在探地雷达数据处理中的应用

为了提高浅层埋藏目标识别的瞬时物理属性的精度,根据极平坦频率特性有限脉冲相应滤波器的思想,首次提出用它作为微分器和延时器组成解析信号瞬时属性估计器（FIFM）,对探地雷达信号的瞬时属性进行提取。结果表明,瞬时属性剖面的分辨率得到了明显的提高;采用瞬时属性估计器进行探地雷达信号瞬时参数的研究是一种非常有效的方法,可用于公路、桥梁、隧道等结构物的无损检测数据分析。     

动态匹配追踪中利用连续相位求取瞬时频率

动态匹配追踪算法将信号的瞬时特征作为先验信息引入到信号分解中,缩小了时频原子库的搜索范围,提高了匹配追踪效率。但在利用瞬时相位获取瞬时频率信息时会得到无物理意义的负频率,不能直接应用于匹配追踪,已有文献并没有给出瞬时频率存在负异常时的解决方案。基于此,笔者通过研究主值相位与解缠相位之间的差异,提出了利用连续相位求取瞬时频率,并推导了连续相位的求取公式;然后通过实例分析,获得了反映三种不同相位数值变化的趋势图;最后通过对比三种相位求取的瞬时频率,证明由连续相位求取的瞬时频率不存在负异常,可直接应用于匹配追踪算法。     

频率补偿技术在提高探地雷达分辨率中的应用

电磁波传播理论与弹性波的传播理论有许多类似的地方,两者遵循同一形式的波动方程,因此可以把石油地震勘探的高频补偿技术引入到探地雷达的资料处理中。但该技术是在对近地表微测井直达波资料分析的基础上对叠后地震数据进行高频补偿的一种方法,而在实际的探地雷达探测中一般都不会同时去做一套微测井资料。基于这样的情况,通过对探地雷达反射子波的分析,总结一套直接利用探地雷达的原始记录就可以做频率补偿的方法,从而达到提高数据分辨率的目的。     

去除探地雷达信号直达波的小波变换方法

简单介绍了二维连续小波变换的基本理论，详细分析了二维连续有向小波变换去除直达波的基本原理，对比了二维Cauchy小波及Morlet小波函数的性质，进而讨论了实际工程应用中小波函数的选择问题。通过实际工程探地雷达资料检验了该方法的有效性。     

基于变分模态分解算法的探地雷达信号去噪研究

探地雷达信号属于非平稳信号,容易受到干扰噪声影响导致探测能力降低.为了消除雷达信号的噪声,经验模态分解(EMD)、总体经验模态分解(EEMD)和完全总体经验模态分解(CEEMD)等方法被应用到雷达数据处理中.EMD虽然不断被改进,但仍存在一些不足,例如容易受到噪声干扰,存在模态混叠现象,依赖经验性等问题.因此笔者采用变...     

探地雷达的应用条件

探地雷达是一种有效的浅部物理勘探方法.从电磁波传播理论角度对其应用条件进行了讨论,指出了导电率、介电常数、探测频率3个主要因素对雷达探测的影响.这对探地雷达的实际应用具有一定的指导意义.     

经验模式分解与小波变换在模拟信号中的对比分析

经验模式分解和小波分解是当前有效处理非平稳信号的两种时频分析方法,它们各具优缺点,适用于不同的领域。模拟信号突变性检测、趋势检测和频率检测的对比实验表明：在突变检测方面,小波分解优于经验模式分解;但经验模式分解在低频信号检测及趋势检测方面优于小波分解。     

二维EMD方法在物化探数据处理中的应用研究——以云南个旧地区为例

文章采用二维EMD方法对云南个旧地区物化探数据进行非线性的多尺度分析与分解,有效分离了物化探异常,揭示深层次找矿信息.证明了二维EMD分解得到的所有分量都具有实际地质意义,它将是一种新的物化探数据处理方法.     

基于经验模态分解的重力异常分离

根据重力异常水平梯度特性,建立了基于经验模态分解的新型重力异常分离方法。通过不同模型实验对比,发现该方法分离所得异常和模型理论异常能很好地吻合,可进行异常定量划分。实测数据处理中,将该方法与平均对数功率谱分析相结合,所分离出的区域异常可以有效描述研究区的区域地质特征。模型试验和实测数据处理都充分表明该方法所分离出的重力异常基本没有畸变和虚假信息产生,具有良好的实际应用价值。     

探地雷达数据的S变换时频分析

S变换自问世以来,凭借其优越性已经被广泛地应用于数字信号处理中。对模拟的探地雷达数据和实测的探地雷达数据进行S变换时频分析。其中探地雷达数据的模拟采用时域有限差分法,地电模型为层状介质,通过S变换时频分析,可以更有效地区分薄层状介质。对实测的探地雷达数据进行S变换时频分析,去除了部分噪音干扰,突出有效信号,图像更直观,易于图像的解译。     

基于经验模态分解的航空重力测量动态误差分离

航空重力测量数据在测量的过程中很容易受到飞机动态运动的影响,飞机动态运动强时,航空重力测量数据的质量也随之降低,虽然经过数字滤波后可以将航空重力异常数据中的大部分噪声去除,但一部分与信号重叠的噪声还是无法被消除.利用经验模态分解对受飞机动态影响较大的一组实测数据进行深化处理.结果表明,在不影响航空重力异常数据分辨率的情况下,该方法很好地消除了由飞机动态运动引起的动态误差,使得航空重力测量的内符合精度由1.43mGal提升到了1.27mGal.     

An empirical mode decomposition based noise cancelation method for potential field data along with a new stopping criterion

Potential field data is generally contaminated by random noise. The high-frequency noise contained in the data brings unfavorable influences to subsequent data processing. Therefore, suppressing the adverse effects of noise has always been a crucial step which is desirable prior to applying other transformations. Over the past decades, numerous mathematical approaches have been proposed for noise cancelation of potential field data. In the work discussed in this paper, the application of the empirical mode decomposition for denoising of potential field data is briefly described, and a new stopping criterion for this filtering method is introduced. Using the proposed method, the empirical mode decomposition is firstly performed on the original potential field data to get numerous intrinsic mode functions corresponding to components with different frequencies. Each intrinsic mode function is subtracted from the original data to get different residual datasets. The correlation coefficients associated with the original data and various residual datasets are calculated and plotted. The inflection point of the correlation coefficient curve is adopted as the last intrinsic mode function to be selected. The new stopping criterion offers a quantitative way to determine which intrinsic mode functions should be removed during filtering and can be easily implemented within the algorithm. Tests on synthetic noisy gravity data demonstrate that the empirical mode decomposition based noise cancelation method along with this new stopping criterion yield acceptable filtering results for potential field data. The newly developed method is also investigated on real gravity data collected over a magnetite zone in Jilin Province, China.     

Multiscale analysis of noisy 3D GPR data using the directional continuous wavelet transform

Here, we present a new technique of noise effect attenuation in the 3D ground-penetrating radar (GPR) data analysis using the 2D directional continuous wavelet transform (DCWT). The proposed technique is based on the application of a Gaussian low pass filter to the modulus of the 2D DCWT for low scales. After application of the low pass filter, maxima of the CWT are mapped for all range of scales. Application to a noisy GPR data shows that the proposed idea can improve GPR data analysis by the continuous wavelet transform.     

应用奇异值分解与二维经验模型分解提取金矿化致矿重力异常

奇异值分解(singular value decomposition,SVD)和二维经验模型分解(bi-dimensional empirical mode decomposition,BEMD)2种方法用于鲁西铜石金矿田的金矿化致矿重力异常提取.通过对比研究得出如下结论:①基于重力数据,由BEMD和SVD 2种方法获...     

Condensing multi-element reconnaissance geochemical data from south Greenland using empirical discriminant analysis

Empirical discriminant analysis classified multivariate data from 2174 geochemical reconnaissance samples from South Greenland, so that they were related to known geological units or characterized as outliers. Training sets, comprising 514 samples from 14 geologic units were selected in order to reflect only the background conditions of each geological unit. A smoothing parameter of 0.5 maximized correct classification of the training sets and extracted a reasonable number of outliers (289, 13% of the samples) representing geographically grouped anomalies. Plots of the geochemical samples classified into the geological units corresponded well to the geological map.Q-mode cluster analysis classified the 289 outliers into 30 groups with different element associations. All types of mineral occurrence known in South Greenland could be recognized amongst the clusters. For example, there were seven clusters which were characterized by samples with high U values and different associated elements each one related to a different type of U mineralization. Another cluster containing samples with high Zr, Nb, and Y values reflects recently discovered pyrochlore mineralization. Other clusters were explained on the basis of geological units which were too small to be mapped or included amongst the training sets.Empirical discriminant analysis successfully reduced the multivariate data to one map, which made it easier to evaluate the varying element levels over the different geological units. Incorrectly classified samples require follow-up in order to appraise the accuracy of the geological mapping. Classification of the outliers by cluster analysis assists both in identifying samples influenced by mineral occurrences and in predicting the type of mineralization to be expected, thereby substantially aiding in the selection of areas for mineral exploration.