Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Channelized and hillslope sediment transport and the geomorphology of mountain belts

This paper uses the results of landscape evolution models and morphometric data from the Andes of northern Peru and the eastern Swiss Alps to illustrate how the ratio between sediment transport on hillslopes and in channels influences landscape and channel network morphologies and dynamics. The headwaters of fluvial- and debris-flow-dominated systems (channelized processes) are characterized by rough, high-relief, highly incised surfaces which contain a dense and hence a closely spaced channel network. Also, these systems tend to respond rapidly to modifications in external forcing (e.g., rock uplift and/or precipitation). This is the case because the high channel density results in a high bulk diffusivity. In contrast, headwaters where landsliding is an important sediment source are characterized by a low channel density and by rather straight and unstable channels. In addition, the topographies are generally smooth. The low channel density then results in a relatively low bulk diffusivity. As a consequence, response times are greater in headwaters of landslide-dominated systems than in highly dissected drainages. The Peruvian and Swiss case studies show how regional differences in climate and the litho-tectonic architecture potentially exert contrasting controls on the relative importance of channelized versus hillslope processes and thus on the overall geomorphometry. Specifically, the Peruvian example illustrates to what extent the storminess of climate has influenced production and transport of sediment on hillslopes and in channels, and how these differences are seen in the morphometry of the landscape. The Swiss example shows how the bedding orientation of the bedrock drives channelized and hillslope processes to contrasting extents, and how these differences are mirrored in the landscape. An erratum to this article can be found at     

GALILEO系统局域完备性监测设计与实现

根据GALILEO系统局域完备性概念和网络RTK的基本理论,建立导航系统局域网络完备性监测理论和算法。算法主要包括网络系统完备性算法和用户完备性算法,内容包括网络误差模型的建立、插值算法的选择、误差改正数完备性指标的计算、完备性指标的验证和用户保护水平的计算等。仿真结果表明,采用网络RTK技术和完备性监测算法,飞机的平面保护水平和垂直保护水平均小于完备性告警门限值,可以满足飞机Ⅱ类和Ⅲ类精密进近的完备性要求,为卫星导航系统在民航中的应用提供完备性理论基础。     

三维地震属性参数在煤层厚度预测中的应用

依托“西部煤炭资源高精度三维地震勘探技术”项目工程,对晋城某矿南翼大巷东南区5m×5m×1ms的三维地震数据体,采用三维地震属性参数预测煤层厚度及其变化规律：沿3煤层、15煤层10ms时窗提取地震属性42种,根据钻孔资料,计算出煤厚与地震属性相关系数;从中优选出相关系数大于0．35的地震属性,其中3煤层9个、15煤层10个;然后进行地震属性互相关分析,优选出与3煤、15煤层厚度相关系数较大的4种属性,建立预测煤厚的BP神经网络模型,分别选取3煤层12个、15煤层4个实测数据作为学习训练和测试样本,以钻孔地震属性作为学习样本,对网络进行训练,最终获得全区煤层厚度。经与预留钻孔成果资料对比,预测精度较高,结果可用。     

VPN技术在煤炭地质档案网络服务中的应用

网络服务是煤炭地质档案业务发展的必由之路,为了解决在公共网络传输过程中的安全问题,本文介绍了一种虚拟专用网络技术,通过分析其应用于煤炭地质档案网络服务中的优势,为行业内外部的信息共享提供了一个新的思路。     

山水城市建设与都市旅游发展——以贵阳市为例

都市旅游为推进城市现代化起到了十分积极的作用,已成为城市竞争力的重要组成部分。把城市建设与都市旅游结合起来,寻求二者的共同点,既可促进都市旅游的发展,又有利于现代城市的建设。通过对贵阳市发展建设和旅游开发条件的分析,从理论和实际结合上探寻贵阳“山水城市”建设与都市旅游资源开发的完美结合点,实现城市建设和旅游发展的双赢。     

南京市首级高程控制网的建立及跨江水准的研究

本文首先介绍了南京市首级高程控制网的概况,接着重点讨论了跨江水准测量方法的选择,并全面分析了测距三角高程法影响精度的三个因素,结合南京市首级高程控制网全面介绍了数据处理的有关问题。     

临港新城地质特征及其对新城规划与建设的影响分析

结合临港新城三维地质调查所取得的成果,分析了该城区地质特征及对新城建设的影响,为新城规划建设提供科学依据和服务。     

北京及邻区微震活动的初步分析

通过对北京遥测地震台网近年来记录到的北京及邻区地震的震中分布,地震活动频度及能量释放强度的分析,得到本区地震活动在时间分布上具有“聚堆性”。在年发震频度,地震强度和能释放方面均具有双峰值特征,并且具有较好的一致性和同步性,在空间分布上具有条带特征,且形成北东～南西和北西～南东的两条相互交汇的条带。又通过统计分析得到本区发震概率最大的时间段是每年的１０月前后,而地震主要发生在北西～南东带上。     

Automatic classification of volcanic earthquakes by using Multi-Layered neural networks

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of explosion quakes at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.