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.     

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

地震和爆破时频域能量分布特征的对比分析

运用小波分析方法计算和分析了CDSN乌鲁木齐台记录的天然地震和人工爆破在时频域的能量分布特征,分析结果表明,在震中距大致相同的范围内,同一台站记录的不同当量的人工爆破具有相似的时频变化规律,而天然地震能量分布却存在较大差别。天然地震与人工爆破的时频域能量分布谱图存在比较明显的差异。     

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.     

日本鬼鱼由工厂化育苗技术研究

从北部湾沿海采捕的日本鬼鱼由 ( Inimicus japonicus)亲鱼 ,经暂养后进行催产 ,雌鱼每千克体重注射 L RH-A3 1 0μg DOM 5mg或 HCG 60 0～ 80 0 IU LRH-A3 3 0～ 4 0μg;雄鱼注射同样药物 ,剂量减半。共采卵 1 0 9万粒 ,平均受精率 89.0 % ,平均孵化率 90 .0 % ,共培育出体长 1 .8～ 2 .3 cm的幼鱼 2 6.8万尾 ,平均成活率 3 0 .7%。     

人工免疫系统与嵌入规划目标的城市模拟及应用

人工免疫系统(AIS) 具有强有力的计算能力, 可以通过免疫识别、克隆选择、免疫学 习、免疫记忆等功能来进行模式识别和自适应学习。AIS 所具有的自学习、自适应和记忆的能力非常适合于复杂地理过程的研究。而元胞自动机(CA) 是研究复杂系统非常方便和有效的工具。将人工免疫系统和元胞自动机相结合, 建立了城市演变的模拟和规划模型。该模型通过改变抗体的进化变异机制, 把规划目标嵌入到AIS 算法中, 抗体将会逐渐朝着规划目标“进化”, 从而模拟出基于不同规划情景的城市发展空间格局, 为城市和土地利用规划提供决 策支持。设计了6 种不同的城市发展方案, 利用该模型模拟了不同规划方案下珠江三角洲城市的发展情景(1988-2002 年)。并比较了不同模拟情景结果城市的紧凑性: “城市中心” 和 “城市中心-高速公路”发展模式的城市形态更为紧凑, 破碎度较低; 而“镇中心” 和“道路”发展模式形成的城市形态则比较凌乱和分散。模拟结果和分析表明: “城市中心-高速 公路”是珠江三角洲最适合的城市发展模式。     

北京市永定河地下水库水资源储蓄能力研究

北京市永定河冲洪积扇中上部,具备建设地下水库的基本条件。本文在确定永定河地下水库水资源人工补给方式的基础上,分析了各方式的入渗能力,提出了地下水库的人工补给方案,利用地下水数值模拟的预测结果和有效储水率计算公式,首次研究了地下水库的储水能力,分析了永定河地下水库的水资源补给效果。结果表明,永定河地下水库水资源人工补给能力最大可达18．93m^3／s,回灌地表水2．42×108m^3的情况下地下水位最大回升32m,地下水储存量增加2．09×108m^3,回灌后1年和5年的有效蓄水率分别为78．6％和46．8％,北京永定河地下水库具有较好水资源回灌能力、储存能力,地下水人工调蓄能够起到明显的效果。     

人工神经网络模型在地学研究中的应用进展

近年来,随着人工神经网络（ANNs）自身技术的不断完善,应用ANNs模型成功解决各类地学问题的案例大量出现。通过对其发展历程进行分析发现,20世纪80年代末国际地学分析中已开始融入ANNs技术,国内则滞后 1～2年。在地学分析中使用的各类人工神经网络类型中,BP模型应用最广,占到85％以上。在10余年的应用过程中,虽然地学的各个分支学科都移植了一种或数种ANNs模型作为其分析工具,但水文、地质、大气、遥感等领域应用较为广泛。传统地学定量分析中的单变量或多变量预测成为人工神经网络地学模型的主要应用客体。同时,诸如模式识别和过程模拟等也是ANNs模型求解的对象。目前,随着建模经验和知识的积累,地学ANNs模型的发展呈现出多种技术综合集成的态势,遗传算法、小波转换、模拟退火算法以及模糊逻辑等方法与ANNs模型融合,成为解决地学分析中非线性问题的利器。     

基于气象因子的华中电网负荷预测方法研究

在分析各种节假日负荷变化规律的基础上, 利用气象因子作预报变量, 使用动态的综合线性回归和自回归相结合的混合线性回归方法及非线性的人工神经网络方法来进行华中电网日负荷和日最大负荷及日最小负荷的预测。对12个月共365天的独立样本试预报表明, 该客观方案对华中电网负荷的预测精度可满足业务调度的需要。     

铁路工程地质选线咨询系统

本文考虑了铁路线位选择的特点，从地质的角度出发，将铁路地质选线中涉及的主要问题（滑坡、泥石流、风沙、岩溶）归纳、总结建立知识库，通过推理为铁路总体选线提供地质依据，以减少工程失误，节约工程造价，保证工期。本文重点介绍该系统的组成部分及各个部分的具本设计思想和设计技巧。