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.     

弹性地基条件下狭窄煤柱岩爆的突变理论分析

将未采煤层视为弹性地基条件下,运用maple9.5符号运算软件解得岩梁的挠曲线方程。根据能量守恒原理得到的功、能增量平衡关系式,求得岩梁–煤柱系统作准静态形变时的平衡方程--折迭突变模型平衡方程;用折迭突变总势能函数所作的稳定性判别表明,岩爆前兆阶段岩梁–煤柱系统处于不稳定平衡状态;煤柱岩爆的条件是Cook刚度准则,该准则可以动态地用煤柱形变增大时所需外界的能量输入率趋于0来表示;计算了煤柱岩爆地震能释放量,阐明煤柱岩爆机制是由于岩梁弹性能释放量超过峰后软化煤柱形变所耗的能量。通过岩梁弹性势能变化率曲线和煤柱耗能曲线,以几何形式描述了所得研究结果。计算表明,未开采煤层刚度变化对岩梁的等效刚度影响微弱。工作面开挖和采空区增大,将显著减小岩梁刚度Ke,对煤柱岩爆有重要影响。     

新疆阿克苏东,西部雹云和非雹云雷达临近指标的统计特征值

根据阿克苏东部沙雅７１１雷达１９９４、１９９５年雷达资料以及西部农一师八团ＸＤＲ－Ｘ型７１１数字化雷达１９９６、１９９７年回波资料,利用多因子雹云判别方程,给出了东西部地区冰雹云及非冰雹云临近指标的统计特征值。     

土壤墒情遥感反演与旱情诊断

土壤墒情与植被生长状况和地表温度之间存在密切联系?贑OST模型算法和单窗算法,开展了TM/ETM+多光谱数据的地表反射率、地表温度(LST)和土壤调整植被指数反演(MSAVI),分析了地表温度和植被指数的线性关系,提出了土壤墒情几何特征指数和旱情诊断函数,结合土壤含水量实测数据,建立了横山县土壤墒情遥感反演模型。实证结果表明,基于TM/ETM+数据反演的长度指数可进行旱情诊断;对土壤含水量的反演模型进行T检验,差异不显著,而基于地面温度的土壤墒情反演模型优于土壤调整植被指数反演模型。     

周期-频度谱在小爆破自动识别研究中的应用

历年来在所开展小爆破识别与判据研究的基础上,进行了计算机易于实现爆炸事件自动识别判据的研究,通过对近震、爆破、矿塌和远震事件的周期-频度谱、波形不规则指数、卓越周期开展的研究,确定了切实可行的爆破事件自动识别方法和判据,达到了77%以上的正确自动识别概率。关键词爆破;自动识别;周期-频度谱;波形不规则指数;卓越周期     

华池油田长3岩性油藏流动单元划分及其合理性验证

提出岩性油气藏流动单元划分及其合理性验证方法。研究中针对华池油田长3岩性油藏的特点,采取控制流体渗流的主要参数,对流动单元进行了划分。同时采用数学方法、微观渗流实验方法、宏观生产动态分析方法对流动单元划分的合理性进行了验证。研究结果表明:1）研究区流动单元可以分为三类,分别为A类、B类和C类。不同流动单元所处相带位置不同,物性也不同。
2）判别分析的方法、渗流实验方法和生产动态分析法是验证流动单元划分合理性的有效手段。研究区流动单元的正判率达95％以上;相同流动单元具有相似的微观渗流特征及生产动态特征,不同流动单元的微观渗流特征及生产动态特征明显不同。
3）C类流动单元及注采井间不同流动单元对接区域是剩余油的主要富集区,应注意挖潜,提高油层采收率。     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

论Logistic判别分析中极大似然估计的存在性

研究Logistic判别分析中的极大似然估计的存在性与唯一性问题,是一个在实际中碰到的难题。本文给出了分离及有重叠数据情况下的结论证明。     

天宝GX200地面三维激光扫描仪鉴别率检定

三维激光扫描技术是一种新兴的全自动、高精度测量技术。三维激光扫描仪能够在短时间内大面积、高密度地获取目标的空间点云数据,逼真细腻地描述目标表面。三维激光扫描仪作为一种测量仪器,本身也存在误差。目前,我国三维激光扫描仪检定技术较为薄弱且尚未制定系统的技术规范。本文结合国内外对三维激光扫描仪性能检定的理论方法,设计相应的实验方案,对所用Trimble GX200扫描仪的鉴别率进行检定。