核爆地震模式识别中的特征相空间研究

从核爆地震模式识别中的最少不相关特征量确定、训练样本的选择出发,介绍了特征空间与相空间等价问题的由来,并通过数值实验验证了特征空间与相空间等价的有效性,确定了特征选择中不相关特征量的下限,而且特征相空间分析方法具有很好的噪声鲁棒性,对于分析实际的模式识别问题极具优势.最后,本文提出了进一步研究的思路和设想.     

利用高分辨率卫星影像进行地震损失评价所需的城市特征识别

高分辨率卫星影像已经在一些国家的民用领域得到应用。利用高分辨率卫星影像来收集地震损失评价所需要的各类城市信息不仅高效和有较好的时间分辨率，而且它可以减少以往所必需的大量的代价高昂且费力的城市调查。基于在印度城市台拉登所做的研究，讨论了如何利用高分辨率卫星影像进行城市特征识别的一些问题，同时也简单论述了利用GIS／RS软件综合所得的数据以便用于地震损失评价的方法。     

基于地震剖面瞬时峰值能量频率的衰减分析与气藏预测方法

由于流体本身的粘滞性和摩擦性,使得地震波在含油气地层中传播时衰减形成低频阴影.通过对叠后地震资料做时频分解,分析地层衰减特征,是当前除了AVO技术之外,比较流行的直接识别流体和气藏的技术.本文基于波动方程正演,模拟了地震波对含油气地层的响应.然后采用连续小波变换分析不同尺度剖面的瞬时能量特征,提取分频剖面上每一个点的峰值能量对应的频率,得到瞬时峰值能量频率剖面.通过分析瞬时峰值能量频率剖面,分析了地震波在地下介质中传播时的衰减特征.最后应用此方法对实际地震剖面进行了分析,验证了其有效性.     

Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques

This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.     

A pattern recognition approach to evaluation of soil liquefaction potential using piezocone data

A pattern recognition approach to liquefacation evaluation is propoesed. The state of any soil layer at a level ground site subject to seismic loads is represented by a pattern in a seven-dimensional feature space and can be classified into one of three classes: liquefiable cohesive soil, and non-liquefiable cohesionless soil. The liquefaction potential of the soil layer can be assessed according to the probabilities of the pattern belonging to the three classes. Training patterns derived from field data (piezocone (CPTU) data and maximum ground acceleration) from sites which liquefied or did not liquefy during earthquakes in New Zealand are randomly chosen to design a pattern recognition system to provide an optimal estimation of the liquefaction potential of any soil stratum of interest. Two recognition systems have been set up to estimate the state-conditional probability density function. One is based on a Parzen window approach in which no knowledge of the probabilistic structure of the training patterns is assumed; the other is based on a parameter estimation approach assuming a multivariate normal distribution. The error rate of recognition by the Parzen window approach is 6·9% when taking the window size as 1·5, and the error rate by the parameter estimation approach, which can be easily, is 7·7%. implemented without reference to our training patterns     

改进直接基于位移的钢筋混凝土框架结构抗震设计研究

直接基于位移法进行抗震设计时,最初结构的刚度和承载力均是未知的,设计中需假定预期设计结构的振型模式和由此得到的结构侧向地震力分布,这样的设计结果会产生一定的偏差.针对此问题,提出了改进直接基于位移的抗震设计方法,即首先初步设计结构,根据初设结构的振型模式等效单自由度体系,结合目标位移反向设计结构刚度,将设计结构的刚度与初设结构刚度比较,进行调整设计,最后进行承载力设计.程序中为减少迭代次数,定义了结构竖向刚度调整系数,用以简化结构弹性刚度的确定过程,从而实现设计结构在相应地震设防水准下满足预期位移的控制要求.最后通过算例证明了该方法具有一定的准确性.     

Improved seismic hazard model with application to probabilistic seismic demand analysis

An improved seismic hazard model for use in performance‐based earthquake engineering is presented. The model is an improved approximation from the so‐called ‘power law’ model, which is linear in log–log space. The mathematics of the model and uncertainty incorporation is briefly discussed. Various means of fitting the approximation to hazard data derived from probabilistic seismic hazard analysis are discussed, including the limitations of the model. Based on these ‘exact’ hazard data for major centres in New Zealand, the parameters for the proposed model are calibrated. To illustrate the significance of the proposed model, a performance‐based assessment is conducted on a typical bridge, via probabilistic seismic demand analysis. The new hazard model is compared to the current power law relationship to illustrate its effects on the risk assessment. The propagation of epistemic uncertainty in the seismic hazard is also considered. To allow further use of the model in conceptual calculations, a semi‐analytical method is proposed to calculate the demand hazard in closed form. For the case study shown, the resulting semi‐analytical closed form solution is shown to be significantly more accurate than the analytical closed‐form solution using the power law hazard model, capturing the ‘exact’ numerical integration solution to within 7% accuracy over the entire range of exceedance rate. Copyright © 2007 John Wiley & Sons, Ltd.     

自然灾害危害度模糊模式识别

本文根据文献[1]提出的相对隶属度概念和模糊模式识别理论，讨论了相对隶属度及模糊模式识别用于自然灾害危害度评价的原理和过程，并结合贵州毕节地区泥石流危害度评价，阐述相对隶属度和模糊模式识别用于自然灾害危害度评价的合理性和可行性，收到满意的效果。     

软土震陷判别方法分析及工程应用

对地基震陷震害及其影响因素进行了简单总结,整理分析了软土震陷的有关规范;结合工程实际钻孔对比讨论了软土震陷不同规范方法的优劣,提出了基于"两台阶的缩小范围量化"的软土震陷判别方法并进行了验证及工程应用。     

正演方法识别滨里海中区块盐下真假构造

在滨里海中区块地震时间剖面上,因高速盐丘的影响,盐丘下伏地层存在上拉现象,造成视觉上的假构造,给解释成图带来困难,也影响着钻探的效率.目前人们用变速成图和深度偏移的方法尚没能完全消除假构造的影响,主要原因是盐丘本身的速度不易精确刻画.在国内,因盐丘地质现象较少,识别盐下真假构造研究工作较少开展;而国外,也只是在最近几年叠前深度偏移技术实用之后,才有了一些研究进展.本文使用模型正演的方法,统计盐丘的平均层速度及其它各层的VSP或测井测量速度制作模型,找到了时间剖面上盐丘厚度与盐下地层向上拉伸量的变化规律.在实际应用中,识别确认了2个盐下真实构造、3个盐下假构造和2个可能真构造;其成果也已用于叠前深度偏移及构造成图分析等方面,并成为以后在类似地区的有效勘探方法之一.     

Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition

The task of locating buried utilities using ground penetrating radar is addressed, and a novel processing technique computationally suitable for on-site imaging is proposed. The developed system comprises a neural network classifier, a pattern recognition stage, and additional pre-processing, feature-extraction and image processing stages. Automatic selection of the areas of the radargram containing useful information results in a reduced data set and hence a reduction in computation time. A backpropagation neural network is employed to identify portions of the radar image corresponding to target reflections by training it to recognise the Welch power spectral density estimate of signal segments reflected from various types of buried target. This results in a classification of the radargram into useful and redundant sections, and further processing is performed only on the former. The Hough Transform is then applied to the edges of these reflections, in order to accurately identify the depth and position of the buried targets. This allows a high resolution reconstruction of the subsurface with reduced computation time. The system was tested on data containing pipes, cables and anti-personnel landmines, and the results indicate that automatic and effective detection and mapping of such structures can be achieved in near real-time.     

饱和砂土地震液化判别的分形插值模型

借鉴分形基本理论,提出了基于分形插值模型的饱和砂土地震液化判别方法.该方法首先选取影响饱和砂土地震液化判别的7个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到40个标准样本,用于构建饱和砂土地震液化判别的分形插值模型;其次根据最大似然分类原则确定每个饱和砂土地震液化判别指标的评价分维数;然后利用加权求和法计算样本的综合评价值,并根据样本综合评价值与经验等级之间的关系建立分形插值评价模型;最后,进行了实例分析结果表明:该模型的评价结果合理、客观,计算得到的每个样本具体得分值,即使对属于同一级的样本也可以给出其地震液化程度的顺序,为饱和砂土地震液化评价工作提供了一种新的研究方法与思路.     

基于深度学习卷积神经网络的地震波形自动分类与识别

发展高效、高精度、普适性强的自动波形拾取算法在地震大数据时代背景下显得越来越重要.波形自动拾取算法的主要挑战来自如何适应不同区域的不同类型地震事件的分类与筛选.本文针对地震事件-噪音分类这一问题, 使用13839个汶川地震余震事件建立数据集, 应用深度学习卷积神经网络(CNN)方法进行训练, 并用8900个新的汶川余震事件作为检测数据集, 其训练和检测准确率均达到95%以上.在对连续波形的检测中, CNN方法在精度和召回率上优于STA/LTA和Fbpicker传统方法, 并能找出大量人工挑选极易遗漏的微震事件.最后, 我们应用训练好的最优模型对选自全国台网的441个台站8天的连续波形数据进行了识别、到时挑取及与参考地震目录关联, CNN检出7016段波形, 用自动挑选算法拾取到1380对P, S到时, 并与540个地震目录事件成功关联, 对1级以上事件总体识别准确率为54%, 二级以上为80%, 证明了CNN模型具有泛化能力, 初步展示了CNN在发展兼具效率、精度、普适性算法, 实时地震监测等应用上具有巨大潜力.

     

Global shear stress pattern in main seismic active belts

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Improved 2-D attenuation analysis for Northern Italy using a merged dataset from selected regional seismic networks

A merged, high-quality waveform dataset from different seismic networks has been used to improve our understanding of lateral seismic attenuation for Northern Italy. In a previous study on the same region, Morasca et al. (Bull Seismol Soc Am 98:1936–1946, 2008) were able to resolve only a small area due to limited data coverage. For this reason, the interpretation of the attenuation anomalies was difficult given the complexity of the region and the poor resolution of the available data. In order to better understand the lateral changes in the crustal structure and thickness of this region, we selected 770 earthquakes recorded by 54 stations for a total of almost 16,000 waveforms derived from seismic networks operating totally or partially in Northern Italy. Direct S-wave and coda attenuation images were obtained using an amplitude ratio technique that eliminates source terms from the formulation. Both direct and early-coda amplitudes are used as input for the inversions, and the results are compared. Results were obtained for various frequency bands ranging between 0.3 and 25.0 Hz and in all cases show significant improvement with respect to the previous study since the resolved area has been extended and more crossing paths have been used to image smaller scale anomalies. Quality-factor estimates are consistent with the regional tectonic structure exhibiting a general trend of low attenuation under the Po Plain basin and higher values for the Western Alps and Northern Apennines. The interpretation of the results for the Eastern Alps is not simple, possibly because our resolution for this area is still not adequate to resolve small-scale structures.     

Experience in seismic signal recording using broadband electrochemical seismic sensors

Development and application of broadband seismic sensors, namely velocimeters of electrochemical type (chemotrons), are considered. The principle of operation and design of modern electrochemical (molecular-electronic) converters of seismic signals for linear and rotary motions are described. The data on metrological certification and prolonged trials of these seismic sensors on test benches are presented. Their advantages as compared to stationary broadband seismic sensors of electrodynamic type lie in impact immunity, high power supply efficiency, operability at large inclinations and in a wide temperature range. Comparative analysis of records of the Sichuan Earthquake (China, May 12, 2008) obtained in Moscow on a test bench at the Institute of Physics of the Earth of the Russian Academy of Sciences with help of an electrodynamic seismic sensor and on a test bench at the Design Bureau of Oceanological Equipment of the Russian Academy of Sciences with the help of an electrochemical sensor is presented in conclusion.     

地震干涉测量法近地表散射波分离技术

针对山地地震勘探数据低信噪比问题,近地表散射波分离意义显得尤为突出,地震干涉测量法为此提供了一种技术手段.本文将地震干涉测量理论和散射理论结合起来,导出了近地表散射波地震干涉测量表达式,分为互相关型和褶积型表达式,它们由实际波场和背景波场干涉测量构成.根据近地表散射波分离理论,结合陆上地震勘探实际观测系统,采用褶积和反褶积混合型地震干涉测量配置,用实际地震资料展示了近地表散射波分离技术的应用效果.经过理论分析和砾石区实际资料试验,表明地震干涉测量不仅能分离测线上散射源产生的散射波,而且能分离部分侧面散射波.该技术的优点在于它适应于起伏地形和不均匀近地表结构,并且不需要起伏地形和近地表速度信息.为了从实际资料中消除近地表散射波,本文采用多道匹配滤波自适应减法,在砾石区见到较好效果.     

深地震反射剖面构造信息识别研究

随着我国深部探测计划的实施,近年来在中国大陆地区实施了多条深地震反射剖面,获得了大量揭示地壳上地幔精细结构的宝贵资料. 为了更充分地挖掘这些资料中所包含的深部构造信息,本文在研究和总结前人工作的基础上,提出一种快速识别深地震反射剖面构造格架的方法. 该方法通过数据预处理、强振幅提取、中值滤波、对象识别、连续性计算和连续性滤波实现对深地震反射剖面构造格架识别. 同时,该方法还可通过对象倾角计算对复杂区域进行属性分析.     

基于卷积神经网络的地震波形智能识别研究

随着世界上多个国家和地区的地震预警系统投入运行,误报和漏报等问题逐渐突显,特别是将标定以及强干扰波形误识别为大震事件,快速、精确地区分地震与其他波形是一个难题。针对于此,该研究提出了基于卷积神经网络地震波形智能识别方法。首先收集并处理了2012—2017年中国境内福建以及周边邻省共683个地震和478个爆破事件,并对这些样本筛选、截取和基线校正等预处理,共得到了27 500条三通道波形。在此基础上,构建了3 s波形输入的卷积神经网络模型(SW-CNN)。结果表明：模型对地震、噪声、爆破和异常波形的识别率分别为97.9、99、99.2和99.3%。相比于人工手动分类识别,该模型更省时和更稳定,为地震预警目前所面临的问题提供了一个新的解决方法。     

Analysis of seismicity of central Luzon by pattern recognition

The distribution of large shallow earthquakes (magnitude ?5.5) in central Luzon, Philippines, related to the underthrusting of the Eurasian plate, is examined by the technique of pattern recognition. The objective of the study is (1) to forecast restricted zones of seismic activity, (2) to determine geological features which, in combination, are associated with the earthquakes in the environment of subduction, and (3) to determine whether more insight into regional seismicity can be gained by pattern recognition studies than by conventional seismicity studies. It appears from this analysis that large earthquakes occur (a) in a zone between the shallower portion of Manila Trench on its landward side and the belt of volcanoes and (b) on large strike-slip faults in the region such as Verde Island Passage fault and the Philippine fault. In these zones, large earthquakes occur at locations identified by large topographic relief in the vicinity and a high degree of fractured crust. In contrast to characteristics in California and central Asia, the intersection of faults nearby is not a major characteristic for large earthquakes in and near central Luzon. Topography and degree of fractured crust are important characteristics in all areas studied so far. Several earthquakes which are known to have occurred were considered unknown, and control experiments were carried out to determine whether the locations of these earthquakes would be identified as having potential for large earthquakes. The results of control experiments indicate that the pattern recognition technique is fairly successful in spatial forecasting if the known historical record is assumed almost complete for various major zones (85% in this case). It fails to forecast if information is totally lacking about earthquake activity on a tectonic feature. In the present study, a control experiment was able to correctly identify the locations of two of the largest earthquakes known to have occurred in the area. It was better able to identify locations of large earthquakes than a simple contouring of known earthquake locations would suggest.