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.     

基于WBS-RBS的地铁基坑故障树风险识别与分析

针对地铁基坑工程环境复杂、影响因素多的特点,提出以故障树分析为基础结合工作分解结构（WBS）-风险分解结构（WBS）进行风险识别的方法。此方法引入WBS-RBS将地铁基坑工程工作分解结构和风险源分解结构耦合判断并说明相应风险因素或风险事件,并按照故障树建树原则根据彼此间的逻辑关系,用逻辑门连接上下层事件,形成地铁基坑工程主要故障树。在此基础上,对风险因素进行敏感性分析并提出相应的预防措施。     

极端干旱区荒漠稀疏河岸林遥感分类研究

研究以位于极端干旱区的塔里木河干流中下游地区为例,基于Landsat TM影像,结合决策树分类、几何光学模型与光谱角匹配,解决混合像元信息分解,实现干旱区稀疏荒漠河岸林类别识别。首先从遥感视角的角度,将地物分解为目标和背景,提出塔里木河干流荒漠河岸林植被分类系统;其次以多变量决策树法将非荒漠植被信息剔除,采用几何光学模型模拟各类荒漠植被的像元光谱,最后以光谱角匹配的方法将荒漠植被进一步进行分解,得到塔里木河干流中下游地区典型研究区的植被分类专题图,分类精度结果表明:基于混合像元分解与几何光学模型的分类方法总精度达到了79.43%,Kappa系数为0.718,表明分类质量良好。     

滑坡次生灾害损失评估方法研究

针对目前滑坡灾害破坏损失评估研究还没有涉及滑坡次生灾害破坏损失评估方面的研究,提出并建立了滑坡次生灾害破坏损失的评估模型与方法。应用故障树分析法（FTA）和事件树分析法（ETA）相结合的因果图方法对滑坡次生灾害作可靠性分析,由此对滑坡次生灾害损失进行了预测及评估。     

弹性地基接缝板声振法脱空判定

采用声振法研究接缝和弹性地基脱空与声学特征变化的关系,利用有限元方法分析了有、无脱空接缝板的振动频率。通过声振法检测,得出地基脱空的范围及大小与声学特征参数具有很好的相关性,以某一脱空路面为例,通过计算分析,提出采用基于信息融合理论的集成神经网络技术对刚性路面脱空状况进行识别。为用声学特征进行地基脱空等缺陷无损检测提供了理论和实验依据。该法速度快、精度高,便于实际应用。     

神经网络BP算法在GPS高程转换中粗差探测的尝试

引入神经网络的BP算法中稳定、收敛速度快的Levenberg—Marquardt算法,进行GPS高程转换中的粗差探测,提出了LM粗差探测法。通过实验做出分析比较,得出了实用可行的计算方法。     

人工神经网络在盐渍土盐胀特性研究中的应用

利用人工神经网络处理非线性体系的优势性,对盐渍土膨胀规律多影响因素试验数据进行了建模方法分析,提出了盐渍土盐胀率随含水量、氯化钠含量、硫酸钠含量、初始干容重和上覆荷载5因素变化的计算公式,计算结论比常规二次回归法更加符合目前对盐渍土盐胀规律的定性认识.     

腾格里沙漠南缘油松树轮宽度变化及其对气候因子的响应

树木年轮定年准确、连续性强、分辨率高,并且易于获取复本,已经成为过去气候变化研究的主要手段之一。在沙漠地区,受气候条件的限制,树轮研究工作开展的较少。通过分析腾格里沙漠南缘油松树木年轮宽度的变化及其对气候因子的响应,发现夏季（6~8月）温度以及年降水量是当地油松生长的重要限制因子。对树木年轮标准化年表的功率谱分析表明,树轮记录具有2.6 a,7.5 a,14 a以及16.5 a的显著周期,其中2.6a以及7.5a周期的树轮指数变化,可能与ENSO的周期有关。     

高陡山区开采自然坡失稳分析的神经网络方法

针对山区地下开采自然边坡稳定性分析问题,根据大量工程实测资料,建立了人工神经网络预测模型。通过对人工神经网络算法的改进,选取适当的动量项系数及变步长方法,对已有的实测资料进行了训练和测试,并对丁家河磷矿自然边坡稳定性进行了具体的预测分析,理论计算结果与工程实际情况一致。分析结果表明,所建立的理论模型可用于山区磷矿开采自然边坡稳定性预测分析。