庐枞矿集区大地电磁探测及电性结构初探

在庐枞矿集区开展大地电磁测深(Magnetotelluric,MT)工作,有助于研究庐枞矿集区区域地质结构、构造,同时,对强干扰地区大地电磁下扰信号规律的研究和MT数据的处理与解释水平的提高有着重要的理论意义.论文首先阐述了庐枞盆地的地质概况及深部MT探测的研究现状,接着介绍了庐枞矿集区大地电磁测深数据采集及相关实验研...     

Analysis of earthquake ground motions using an improved Hilbert–Huang transform

Some limitations of the Hilbert–Huang transform (HHT) for nonlinear and nonstationary signal processing are remarked. As an enhancement to the HHT, a time varying vector autoregressive moving average (VARMA) model based method is proposed to calculate the instantaneous frequencies of the intrinsic mode functions (IMFs) obtained from the empirical mode decomposition (EMD) of a signal. By representing the IMFs as time varying VARMA model and using the Kalman filter to estimate the time varying model parameters, the instantaneous frequencies are calculated according to the time varying parameters, then the instantaneous frequencies and the envelopes derived from the cubic spline interpolation of the maxima of IMFs are used to yield the Hilbert spectrum. The analysis of the length of day dataset and the ground motion record El Centro (1940, N–S) shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful and readable Hilbert spectrum than the original HHT method, short-time Fourier transform (STFT) and wavelet transform (WT). The analysis of the seismic response of a building during the 1994 Northridge earthquake shows that the proposed method is a powerful tool for structural damage detection, which is expected as the promising area for future research.     

2008年3月21日新疆于田7.3级地震发震构造与震前地震活动特征研究

2008年3月21日新疆于田发生7.3级地震,打破了中国大陆6年多的7级地震平静,成为我国近期较为显著的一次地震事件.综合分析MS≥4.0余震分布、震区断裂性质以及等震线长轴方向等资料,认为郭扎错断裂是这次地震的发震构造;据Harvard震源机制解分析,这次地震是在近NS向力的作用下,郭扎错断裂发生略带走滑分量的拉张破裂所致.通过地震前震区附近地震活动特征分析发现,于田地震发生在1996年以来新疆南部及邻区7级地震有序分布的空段;震前震区附近有4级地震空区形成,空区持续91个月后发生了2006年9月12日皮山5.8级"信号震",其后1.5年发生于田7.3级地震.     

Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.     

华北地区强震前的信号震及其预测意义

根据对华北地区1970年以来M_S≥6地震以前中小地震活动空间图像变化特征的分析, 研究了“信号震”发生的时空特征及其地震活动背景, 由“场-源”关系特征对一般地震进行严格的筛选识别, 从而得出信号震的有关预测指标。 信号震一般发生在强震前的2年之内, 多数发生在15个月内; 信号震与强震的距离不超过200 km, 多数在100 km之内; 震级强度一般为M_L4.0～5.3。 信号震一般发生在局部的M_L≥4.0地震平静区内, 一般发生在中小地震条带上或条带附近, 在其周围或附近存在中小地震空区。 检验结果表明, 信号震发生后的9个月之内, 其预测概率P_t即超过0.5, R_t值达到0.27; 预测区域半径在距信号震震中100 km之内时, 其发生概率P_d可以达到0.73; 预测强震震级一般为M_S≥6.0。 研究表明, 信号震的环境应力值τ₀明显高于其他地震, 显示了高应力背景的异常显著性, 它所辐射的地震波中很可能含有未来强震孕震区的大量的本质性信息。     

陡倾斜脉状矿体隐伏深度计算

在研究广西 10 0个矿区地质资料基础上 ,建立了陡倾斜脉状矿体隐伏深度计算经验公式 ,从而为隐伏矿床预测、减少矿床勘查风险提供了信息     

风廓线雷达估算大气返回信号功率方法研究

基于风廓线雷达大气返回信号功率谱中噪声电平的估算方法,统计分析了北京延庆对流层风廓线雷达（CFL-08）2006年10～12月的探测数据。对该频段风廓线雷达环境噪声的空间和时间变化进行了分析,观测期间环境噪声在5km以下随高度递减,10月的平均环境噪声大于11月、12月的平均分布。给出目前风廓线雷达用信噪比估算大气返回信号功率的两种方法,并对两种方法进行了环境噪声的剔除,经过修正后的大气返回信号功率输出结果趋于一致。     

A Physically-Based Turbulent Velocity Time Series Decomposition

Single-point, three-component turbulent velocity time series data obtained in the atmospheric boundary layer over the ocean reveal coherent structures that are consistent with a model of a steady linearly varying spatial velocity field that translates past the measurement point at constant velocity. The kinematic model includes both strain and rotation rates and has implications regarding vortex generation, vortex pairing, vortex break-up, and stability. While the complete specification of the dimensions, spatial velocity gradients, and translational velocity of the linear coherent structure (LCS) cannot be made from the single-point, three-component measurements, the model LCS velocity time series can be determined from least- squares fits to the data. The total turbulent kinetic energy is used to find in the record the initial and final times of a model LCS in the data, i.e., the time interval over which a model LCS is passing over the anemometer. Maxima in the kinetic energy removed from the data (by subtraction of the model LCS velocity functions from the data) are used to identify the most-energetic model LCSs. These model LCS velocity functions replicate the essential large-scale features of the time series of the three-component velocity fluctuations, most noticeably in the streamwise component. The model LCS decomposition was used to perform a scale analysis of the data, which was compared to the usual Fourier method. Time intervals of model LCSs were found successively in the data, after subtracting the previous fits. This process resulted in a series of 'levels with a number of LCSs found at each level. About six levels account for most of the kinetic energy. The model also allows the computation of the Reynolds stress components, for which six levels also are sufficient. The recomposition of the time series on a LCS-by-LCS basis compares well with the mode-by-mode Fourier recomposition for the average momentum fluxes and kinetic energy.     

面波频散谱多模式高分辨率成像的多道信号比较法

高分辨率的面波频散谱成像是浅层地震勘探领域基于频散性质反演横波速度结构中的一个关键步骤.在天然地震探测领域,仅利用两个台站记录的线性信号比较法(LSC),被广泛用来计算面波的频散谱,并用于大尺度的面波层析成像.然而互相关的成像方式会造成频散谱在低频端较低的分辨率.非线性信号比较法(NLSC)利用指数函数克服了这个问题,同时极大地提高了频散谱的成像分辨率.然而,在研究中我们发现,仅利用两个台站的地震记录,并不能将面波的频散特性完整地考虑在内,导致LSC和NLSC方法对高阶模式的成像存在较大的误差.由于主动源面波勘探多道采集的方式,基于信号比较理论的多道信号比较法(MSC)充分利用多道地震信号,可以获得准确的多模式成像,然而该方法需要计算任意两道的频散谱并叠加,存在冗余的计算,导致计算效率较低.因此,本研究对MSC方法进行了相应的改进,通过追踪炮集记录上的面波波组提高了原方法的计算效率,同时,利用理论频散曲线进行叠加分析,验证了改进的MSC方法的正确性和有效性.通过与相移法、LSC和NLSC方法的对比分析,展示了MSC方法是一种准确的、高分辨率的面波多模式频散谱成像方法.实际地震资料的应用...     

基于超宽带雷达及支持向量机的灾后人体呼吸信号识别方法与试验研究

与常规雷达相比,超宽带雷达具有距离分辨力高、近距离盲区小、穿透性强、目标识别率高等特点,已被广泛应用于灾后搜寻、救援工作中,以对受困生命体征目标进行生命探测。为实现使用超宽带雷达对受困生命体征目标的识别定位,本研究提出基于信号多特征提取技术及支持向量机模型的人体呼吸信号识别方法。首先,使用经验模态分解、变分模态分解及希尔伯特变换提取雷达探测信号的微多普勒特征,使用傅里叶变换提取宏观频谱特征,使用相关分析获取相关性特征;然后,以提取的信号特征为输入,使用支持向量机模型对信号进行分类,进而对人体呼吸信号进行识别,对人体位置进行定位。不同障碍物场景下的试验结果表明,本方法可有效识别砖墙、建筑楼板等遮挡物下的受困生命体征目标,并提供其位置信息。