地震事件初动点的自动判定研究

研究了地震信号到来前背景噪声的波形规律,建立起表征背景噪声的自回归模型,并编制了相应的建模程序和地震信号初动点自动判定程序．对25次地下爆炸地震事件的初动点进行判定,并给出了自动判定和人工判读的结果．      

关于提高微震事件自动识别效率的一个注记

利用美国国家地震信息中心的地震早期警报系统，通过对数字化地震记录的分析，针对各种可能出现的情况，对微震的自动识别和计算机实时处理进行了讨论。有关结论对地震的自动识别和震源参数的计算测定精度的提高，有重要的参考意义。     

基于能量比的天然地震与人工爆破自动识别算法研究

研究了从天然地震和人工爆破事件的波形记录中提取出来的能量比特征在天然地震和人工爆破事件的自动识别中的有效性及适用性。对波形记录进行了4层小波变换,然后对变换得到的小波系数提取能量比特征,最后利用支持向量分类机ν-SVC进行识别效果检验。实验证明,由bior2.2小波包分解后提取出来的能量比特征对天然地震和人工爆破事件的识别效果很好,可用于实际的自动识别系统作为识别判据之一。     

云计算环境下震前震源异常次声波自动识别方法

云计算下采用三点阵次声源定位方法,在自动识别震前震源次声波过程中不能自动筛选识别大量的异常次声波数据,导致震前监测准确度不高且效率低下。因此提出一种云计算环境下震前震源异常次声波自动识别方法,构建JNS异常次声波数据采集筛查模组,全天候实时扫描访问端口,快速反馈异常次声波数据,采用NDS异常次声波数据序列异常检测算法快速识别错误序阵,准确回查、定位和锁定异常次声波数据;利用震前震源异常次声波自动识别方法分类识别异常次声波信号,判断该信号是否是地震可疑信号。实验结果表明,所提方法可有效自动识别震前震源异常次声波信号类型,信号分类准确率最大值达到99.99%;多次识别耗时最大均值仅为1.3min,具有准确率高和效率快的优势。     

基于CNN的地震前兆台网观测数据异常图像识别方法

地震前兆台网观测数据异常图像识别方法一直是地震监测预报人员研究的重要课题.为提高异常图像识别的工作效率,充分利用已有的异常图像识别经验知识,开展基于卷积神经网络(CNN)的快速异常识别方法探索性研究.结果表明:基于CNN的异常图像识别方法准确率较高,实现了异常图像的快速识别.整个台网的异常图像丰富多样,影响较多.由于特...     

基于小波包和峰度赤池信息量准则的P波震相自动识别方法

基于小波包变换和峰度赤池信息量准则(AIC), 提出了一种新的自动识别P波震相的综合方法, 即小波包-峰度AIC方法． 首先对由加权长短时窗平均比(STA/LTA)法粗略确定的P波到时前后3 s的记录进行小波包三尺度的分解与重构, 分别计算每个尺度重构信号的峰度AIC曲线并将其叠加, 叠加曲线的最小值则为P波震相到时; 然后对原始地震记录进行有限冲激响应自适应滤波以提高信噪比和识别精度; 最后将小波包-峰度AIC方法应用到合成理论地震图及实际地震记录的P波初至自动识别中. 结果表明: 初至清晰度对识别精度的影响比信噪比对其影响更大; 与单独使用加权STA/LTA方法和峰度AIC法相比, 小波包-峰度AIC法具有更强的抗噪能力, 识别精度更高; 当初至清晰时, 小波包-峰度AIC法自动识别与人工识别的P波到时平均绝对差值为(0.077±0.075) s．      

POINTE AUTOMATIQUE*

The automatic picking of seismic sections replaces the slow and painstaking task of manual reflection plotting with rapid and economical processing by computer. Essentially, reflection picking is a decision-making problem which also can be considered as a sort of filtering. Mathematically this means multiplying the seismic data matrix by a more or less complex decision operator. For the sake of economy, we give an initial solution for simple cases which only require easily automatized decision-making criteria. We describe another more elaborate method for use in examining seismic phenomena which are more difficult to spot. The process used is based on the recognition of shapes and consists first of all of determining a series of characteristics capable of identifying each reflection on each trace and then of comparing these different characteristics from trace to trace so as to be able to judge the continuity or discontinuity of the reflections. Automatic picking thus leads to a schematic time-section in which only the horizons found by calculation are retained. An automatic migration program then transforms this time-section into a depth-section.     

基于振幅和瞬时频率的震相自动识别方法

为了充分反映地震信号振幅增大和频率改变的特征,发展了一种以振幅和瞬时频率比乘积为特征参量的震相自动识别方法。谐波信号和有限实际地震记录的应用表明,本方法具有较高的识别精度。     

IDENTIFICATION AND ANALYSIS OF NATURAL CHANNEL NETWORKS FROM DIGITAL ELEVATION MODELS

The identification and analysis of natural channel networks from digital elevation models are discussed from the point of view of their environmental applications. An interactive, graphical software package that implements some of the most widely used techniques for the automatic recognition of channel networks and for the computation of some useful geomorphologic indices and functions is presented.     

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.     

ANN测井相分析方法研究

本文研究了用于测井相分析识别岩性的人工神经网络（ＡＮＮ）模型设计并在ＳＵＮ工作站上用基于距离Ｄ－ＫｏｈｏｎｅｎＮＮ、Ｄ－ＢＰＮＮ两个网络建立了ＡＮＮ自动测井相分析系统。在实际应用中对比了ＡＷ岩相识别和传统多元统计岩相识别的效果，证明了ＡＮＮ模式识别技术用于测井相分析的可行性和优越性。     

基于智能语音技术的闪电哨声波自动识别

闪电哨声波是一种重要的电磁波动,了解其传播特征及传播过程有助于揭开圈层电磁耦合机理.从卫星观测资料识别闪电哨声波通常需要将原始电磁波形进行滤波处理再转化为时频图像,最后采用目视方法识别图像中的色散状形态,整个过程消耗大量人机时间和内存资源,不能满足张衡一号(ZH-1)卫星观测的海量电磁场数据处理的需求.针对该问题,鉴于...     

A fast automatic identification method for seismic belts based on distance correlation and its earthquake case*

Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes. Through the research of earthquake cases, researchers have a relatively consistent method to determine the clarity of an identified seismic belt, but there is still a lack of method on seismic belt identification from the distribution of scattered points. Due to the complexity of exhaustive algorithm, the rapid automatic identification technique of seismic belts has been progressing slowly. Visual recognition is still the basic method of seismic belt identification. Based on the algorithm of distance correlation, this paper presents a fast automatic identification method of seismic belts. The effectiveness of this method was proved by 100 random earthquakes and an example of seismic belts of magnitude 4.0 before the 2005 Jiujiang M5.7 earthquake. The results show that: ① the automatic identification of seismic belts should first identify the “relational earthquake”, then identify the “suspected seismic belt”, and finally use the criterion of seismic belt clarity to determine; ② random earthquakes and real earthquakes identification results show that the distance correlation method can realize the fast automatic identification of seismic belts by computer.     

Spatial texture based automatic classification of dayside aurora in all-sky images

A spatial texture based representation method including features of intensity, shape and texture, was utilized to characterize all-sky auroral images. The combination of the local binary pattern (LBP) operator and a delicately designed block partition scheme achieved both global shapes and local textures capabilities. The representation method was used in automatic recognition of four primary categories of discrete dayside aurora using observations between years 2003–2009 at the Yellow River Station, Ny-Ålesund, Svalbard. The supervised classification results on labeled data in 2003 were in accordance with the labeling by scientists considering both spectral and morphological information. The occurrence distributions of the four categories were obtained through automatic classification of data between 2004–2009, which confirm the multiple-wavelength intensity distribution of dayside aurora, and further provide morphological interpretation of auroral types.     

基于卷积神经网络的速度大脉冲识别方法研究

含速度大脉冲的强地震动具有复杂的特性,人工提取速度大脉冲特征的方法较繁琐,故利用卷积神经网络（CNN）在图像特征自动提取方面的优势,提出基于卷积神经网络图像识别的速度大脉冲识别方法。基于美国太平洋地震工程研究中心NGA-West1数据库提供的强地震动记录,筛选出6 000条非脉冲记录和91条含有速度大脉冲的强地震动记录。采用在原始记录中加入高斯噪声和过采样的方法,使2类记录样本数量达到均衡。利用本文建立的卷积神经网络模型对2类记录速度时程图进行特征自动提取和分类识别,结果显示测试集准确率为99%,表明本文卷积神经网络模型能够自动提取速度大脉冲特征,进而复现已有结果。将本文方法与传统方法进行了对比,结果表明,对含有多个速度脉冲的强地震动记录的识别,本文方法优于传统方法,具有较高的可靠性、鲁棒性、灵活性。     

Automatic signal detection based on support vector machine

Introduction The automatic processing of continuous seismic data is important for monitoring earthquake, in which real data recorded by field stations located in different regions is transmitted to data cen- tre through internet or satellite communication systems. Automatic processing will run firstly on data, afterwards these automatic processing results will be reviewed and modified. The load of interactive analysis would be increase if there were more false events or missed events after run…     

Investigation into the automatic recognition of time series precursor of earthquakes

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Continuous Hidden Markov Models: Application to automatic earthquake detection and classification at Las Canãdas caldera,Tenerife

A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested. Also recently the zone close to Las Canadas caldera shows unusual high number of near (< 25 km), possibly volcano-tectonic, earthquakes indicating signs of reawakening of the volcano putting high pressure on the risk analyst. Certainly for both tasks consistent earthquake catalogues provide valuable information and thus there is a strong demand for automatic detection and classification methodologies generating such catalogues. Therefore we adopt methodologies of speech recognition where statistical models, called Hidden Markov Models (HMMs), are widely used for spotting words in continuous audio data. In this study HMMs are used to detect and classify volcano-tectonic and/or tectonic earthquakes in continuous seismic data. Further the HMM detection and classification is evaluated and discussed for a one month period of continuous seismic data at a single seismic station. Being a stochastic process, HMMs provide the possibility to add a confidence measure to each classification made, basically evaluating how “sure” the algorithm is when classifying a certain earthquake. Moreover, this provides helpful information for the seismological analyst when cataloguing earthquakes. Combined with the confidence measure the HMM detection and classification can provide precise enough earthquake statistics, both for further evidence on the interaction between seismic noise and (volcano-) tectonic earthquakes as well as for incorporation in an automatic early warning system.     

基于DMIS的断层三维表面形态自动检测方法研究

针对断层的测量需求,利用计算机控制技术对空间曲面自动测量程序进行优化,以DMIS(Dimensional Measuring Interface Specification)为开发平台分别形成四边形键入坐标式自动检测程序和任意多边形自动检测程序,且通过所述程序完成塔里木盆地塔中26井区某断层模型表面形态的仿真,给出两种针对断层三维表面形态检测的建系方法:模型建系法和机床建系法。并将空间曲面的自动测量技术应用到断层的三维表面形态测试中,形成一套针对断层复杂表面形态的自动检测方法,使得断层的三维表面形态可以通过上述程序自动测量,且可以使用多个测头角度连续进行一次测量。该方法可以克服在传统的断层检测过程中边界不能完全衔接的问题,提高断层测量的效率和自动化程度,降低测量过程中的人为因素和后期的数据处理难度。     

叠后地震属性分析在油气田勘探开发中的应用

地震属性分析技术一直是地震特殊处理和解释的主要研究内容.随着油气勘探开发的发展,地震属性分析技术已经成为油藏地球物理研究的核心内容,是勘探地震与开发地震之间纽带.本文针对鄂尔多斯盆地的低幅度构造、低孔隙、低渗透率、致密性隐蔽油气藏的特点,综合应用相干数据体分析、地震相自动分类定性识别砂体厚度、地震振幅属性分析、频谱分解、多井约束的储层叠后反演等叠后属性分析技术,探索了一套适合该区油气特征的储层横向预测及油气识别模式.为该区油气勘探开发,储量计算提供可靠依据.同时也为隐蔽性油气藏的勘探开发积累了经验.