Feasibility of signal enhancement with multiple grounded‐wire sources for a frequency‐domain electromagnetic survey

Frequency‐domain electromagnetic methods with a grounded‐wire source are powerful tools in geophysical exploration. However, the signal may be too weak to guarantee the quality of survey data in complex electromagnetic environments, especially when the receiver is located in the air for the newly developed grounded‐source airborne frequency‐domain electromagnetic method. In this paper, a signal enhancement method with multiple sources is proposed to solve this problem. To evaluate the signal enhancement effect, we compared the signals generated by a single source and multiple sources with equal electric moment. The signal differences caused by synchronisation error and separation distance between source elements were analysed, and the methods to achieve maximum signal were introduced. Besides, we discussed the interaction between adjacent source elements to ensure the system safety, including the changes in output current and the safe distance between two sources using a dual‐source model. Lastly, a comprehensive field experiment was designed and conducted to test the multiple‐source method. The data processing results are comparable for single and dual sources, and the signal‐to‐noise ratio of dual source is higher in the field test. The subsurface resistivity structure at the test site is consistent with the previous controlled‐source audio‐frequency magnetotellurics method. These results show that signal enhancement with multiple sources is feasible. This study provides guidance to the application of multiple sources in field surveys when the survey environment is complex and rigorous.     

基于改进型BP网络的空间电场分类研究

空间电场信号异常识别是研究地震引起电离层扰动的重要内容。 将空间超低频电场电位数据看作随机数字信号, 以均值、 均方差、 偏度和峰度等四个指标进行描述, 采用“5·12”汶川大地震前空间超低频电场电位数据作为原始数据, 训练改进型BP神经网络, 建立了空间电场信号异常分类识别模型, 并以SOM神经网络进行验证。 计算结果显示, 空间超低频电场电位异常信号主要集中在5°~25°N, 88°~120°E之间的区域, 汶川大地震影响范围内的电离层扰动, 可能是汶川地震发生前引起的, 这与前人研究一致, 说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的。     

蒸发波导对GNSS海面反射信号有效散射区域的影响

蒸发波导在海洋低空的发生概率高达90%,对舰船雷达、通信等电磁系统具有重要影响.为了分析利用GNSS卫星海面反射信号的时延-相关功率波形反演蒸发波导的可行性,本文提出了GNSS卫星海面反射信号的有效散射区域的概念,并将有效散射区域内的GNSS反射信号区分为GNSS标准反射信号和GNSS波导反射信号;然后,利用射线追踪方法,仿真分析了GNSS卫星海面反射信号的有效散射区域大小对蒸发波导的关键参数——波导高度的敏感性,并分析了在时延-相关功率波形上利用反射信号的传播时延将二者分离开的可行性.结果表明,GNSS卫星海面反射信号的有效散射区域对蒸发波导高度非常敏感,对于2～25m高的GNSS反射信号接收天线,当蒸发波导的高度由0m增加至20m时,GNSS反射信号有效散射区域半径的均值可由约14km迅速扩展至约160km;采用高度角足够大的GNSS卫星可以将有效散射区内的GNSS标准反射信号与GNSS波导反射信号在时延-相关功率波形上分离开.     

2009年夏季极区电离层加热实验观测研究

2009年夏季,中国在挪威特罗姆瑟(Tromsφ)利用非相干散射雷达进行电离层加热实验。实验中发现了处在150km附近的小范围温度增强事件和150—400km之间的大范围温度增强事件。前者的相对增幅明显,后者的绝对增量更大。对这两类加热事件来说,温度增量百分比均随加热功率的增加线性增长,但对比两类加热事件来看,增长速度随加热频率增大而减小。加热效应存在显著的二维分布特征,加热最强方向处在场向附近。加热效应受加热天线波束指向与地磁场的夹角影响明显,随夹角增大温度增强效应减弱。     

GPS第三频载波及民用码的信号质量分析

介绍了L5载波的特点,分析了L5载波对GPS测量的影响,用实测数据分析了三频载波的信噪比及各个民用码的噪声水平。     

一种边缘特征增强的多源遥感影像融合方法研究

基于分频分策略的融合思想,提出了一种边缘特征增强的多源遥感影像融合方法。实验证明,融合后的影像不仅在很大程度上保留了多光谱影像的光谱特征,还提高了空间分辨率,特别是边缘特征得到进一步增强。     

探讨图像增强中直方图均衡化的应用

图像增强是数字图像的预处理,能有效地改善图像整体或局部特征。主要研究了直方图均衡化的图像增强的基本原理,并给出了相关推导公式和算法,同时以一个灰度图像为例,在VC++集成开发环境下实现了直方图均衡化的增强处理,并给出了实验结果图像及直方图。     

面向单脉冲信号分类的集成特征选择与评价

受大量射频干扰信号影响, 快速从海量观测数据中准确识别出单脉冲信号已成为天文数据处理的一项重要任务, 而设计和提取有效数据特征, 是利用机器学习进行单脉冲信号高效识别的决定因素. 针对如何选择最优特征, 进而提升单脉冲信号的分类精度这一关键问题, 设计了面向单脉冲信号分类的集成特征选择方法. 方法首先混合单脉冲信号的参数特征、统计特征和抽象特征, 然后分别利用5种单一特征选择方法选出各自的最优特征集, 最后利用贪心策略对5种单一方法获取的最优特征集进行集成筛选, 获取最优集成特征集. 实验表明, 最优特征集合既包含统计特征也包含抽象特征. 在相同特征数量下, 利用集成特征选择比单一特征选择能获得更高的模型精度, 可使F1值最高提升1.8%. 在海量数据背景下, 集成特征选择对减少特征数量、提升分类性能和加快数据处理速度具有重要作用.     

基于Seislet-TV双正则化约束的地震随机噪声压制方法

人工地震数据总是受到随机噪声的干扰,地震数据时-空变的特性使得常规去噪方法处理效果并不理想,容易导致有效信号的损失.目前广泛应用的预测滤波类方法存在处理时变数据能力不足的问题.随着压缩感知理论的不断完善,稀疏变换阈值算法能够解决时变地震数据噪声压制问题,但是常规的稀疏变换方法,如傅里叶变换,小波变换等,并不是特殊针对地震数据设计的,很难提供地震数据最佳的压缩特征,同时,常规阈值算法容易导致去噪结果过于平滑.因此开发更加有效的时-空变地震数据信噪分离方法具有重要的工业价值.本文将地震数据信噪分离问题归纳为数学基追踪问题,在压缩感知理论框架下,利用特殊针对地震数据设计的VD-seislet稀疏变换方法,结合全变差(TV)算法,构建seislet-TV双正则化条件,并利用分裂Bregman迭代算法求解约束最优化问题,实现地震数据的有效信噪分离.通过理论模型和实际数据测试本文方法,并且与工业标准FXdecon方法进行比较,结果表明基于seislet-TV双正则化约束条件的迭代方法能够更加有效地保护时-空变地震信号,压制地震数据中的强随机噪声.     

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.