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.     

爆夯动力固结法加固软基试验研究

爆夯动力固结法是一种爆炸作用和上覆荷载相结合的新型地基处理方法。其作用原理是利用条形炸药在设置有排水通道的软土地基中爆炸产生高能量瞬变荷载使土体结构发生变化,软土中超静孔隙水压力急剧上升、消散,并在上覆荷载的作用下使土体快速排水固结。在广东佛山合顺－北滘公路干线拟建路段进行的现场试验和室内试验均表明了其加固软土地基的有效性,并结合沉降数据推算了爆夯设计参数,确定本次试验爆夯效果相当于3.2 m高的堆载。     

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.     

爆炸荷载作用下裂隙岩体内损伤范围的观测研究

通过电法CT成像技术,探测微量炸药爆炸前后裂隙岩体深孔之间电阻率的变化,查明探测切面内的岩体损伤裂隙发育形态和空间展布。通过实验可知：微量炸药爆炸后在裂隙岩体的炮孔深部可以形成一定范围、一定数量和一定宽度的裂隙,其中裂隙形成的区域主要集中在炮孔口深的2/3处,当炮孔深度3 m,装药量为330 g时,在眼深1.5～2.5 m处裂隙较为发育,炮孔间距为1.2 m～1.5 m时,可在岩体内部形成贯通裂隙。     

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。     

空孔直眼掏槽成腔模型理论与实践分析

空孔直眼掏槽是隧道或井巷掘进的主要方法之一。基于典型空孔掏槽炮眼布置,分析了掏槽眼爆破形成空腔的物理力学过程;建立了空腔形成的物理和力学模型;研究了腔内碎石在高压爆生气体作用下的抛射过程;得出了空腔尺寸的理论计算方法。槽腔岩石介质破碎和外端部漏斗形成取决于掏槽眼装药爆炸动作用;碎石的抛出率与炸药性质、岩石性质和爆破参数有关。理论分析与在某矿进行的48次试验结果有较好的吻合性。     

爆破震动对土质边坡动力稳定性影响研究

采用拟静力化的惯性力法,利用改进了的条分法来计算主动土压力,并考虑爆破地震的时程效应,提出了已有边坡在爆破过程中的动力稳定性系数的新的计算方法。实例证明,所得计算结果综合考虑了爆破震动波传播过程的衰减、频谱结构及相位差等因素的影响,因此该方法在土质边坡稳定分析中与拟静力法相比更具理论基础,其结果也更接近工程实际。     

岩体隧道锚固作用分析

在工程实例中利用数值模拟方法对考虑和不考虑松动圈的岩体,在不同工况下锚杆加固前后的径向、环向应力,径向、环向位移以及锚杆轴向应力进行全面比较分析。研究锚杆和松动圈对隧洞围岩力学性质的影响,结果表明考虑和不考虑松动圈相比,洞室表面径向位移要明显大一些,而且外界载荷越大,二者的差值越大;在锚杆加固作用下环向应力峰值无论是否考虑松动圈,其出现位置都较加固前向洞壁方向移动,同时围岩径向位移在加固前后变化很大,随着外界载荷的增大,径向位移减小越明显。锚杆支护的主要作用之一是控制围岩的变形和发展,通过锚杆加固改善应力环境来提高围岩的稳定性。     

立井冻土掘进爆破技术的研究与应用

崩落爆破通常承担整个立井冻土掘进爆破40 %～60 %的破土量。宽孔距崩落爆破能够增加自由面的有效利用、增强自由面应力波的反射拉伸作用和消除或减小冻土中的应力降低区。在理论分析的基础上进行冻土爆破漏斗模型试验,综合考虑爆破漏斗体积和爆破后两炮眼间最大凸量,初步认为炮眼密集系数 =1.5左右时较为合理。结合立井崩落炮眼成圈弧形布置和冻土所受夹制作用相对较大的特点,建议应在此基础上适当降低立井冻土崩落爆破炮眼密集系数,这在进一步的工程实践中得到了验证。