Spatial behaviour of Rayleigh waves in layered half‐spaces under active surface sources

In the free state, Rayleigh waves are assumed to travel in the form of planar wavefronts. Under such an assumption, the propagation behaviour of the modes of Rayleigh waves in layered half‐spaces is only frequency dependent. The frequency behaviour, which is often termed as dispersion, is determined by the shear wave velocity profile of layered soils within the depth related to wavelength (or frequency). According to this characteristic, the shear wave velocity profile can be back‐analysed from the dispersion. The technique is widely used in the surface wave testing. However, the wavefronts of Rayleigh waves activated by the surface sources are non‐planar. The geometric discrepancy could result in Rayleigh waves manifesting distance‐dependent behaviour, which is referred to as spatial behaviour in this paper. Conventional analysis ignoring this spatial behaviour could introduce unexpected errors. In order to take the effects of sources on the propagation behaviour into account, a new mathematical model is established for Rayleigh waves in layered elastic media under vertical disc‐like surface sources using the thin‐layer method. The spatial behaviour of the activated modes and the apparent phase velocity, which is the propagation velocity of Rayleigh waves superposed by the multiple modes, are then analysed. Aspects of the spatial behaviour investigated in this paper include the equilibrium path, the particle orbit, and the geometric attenuation of the activated Rayleigh waves. The results presented in this paper can provide some guidelines for developing new inverse mathematical models and algorithms.     

Occurrence and distribution of bacteria indicators,chemical tracers and pathogenic vibrios in Singapore coastal waters

Water quality in Singapore's coastal area was evaluated with microbial indicators, pathogenic vibrios, chemical tracers and physico-chemical parameters. Sampling sites were grouped into two clusters (coastal sites at (i) northern and (ii) southern part of Singapore). The coastal sites located at northern part of Singapore along the Johor Straits exhibited greater pollution. Principal component analysis revealed that sampling sites at Johor Straits have greater loading on carbamazepine, while turbidity poses greater influence on sampling sites at Singapore Straits. Detection of pathogenic vibrios was also more prominent at Johor Straits than the Singapore Straits. This study examined the spatial variations in Singapore's coastal water quality and provided the baseline information for health risk assessment and future pollution management.     

IM‐based and EDP‐based decision models for the verification of the seismic collapse safety of buildings

Decision models for the verification of seismic collapse safety of buildings are introduced. The derivations are based on the concept of the acceptable (target) annual probability of collapse, whereas the decision making involves comparisons between seismic demand and capacity, which is familiar to engineering practitioners. Seismic demand, which corresponds to the design seismic action associated with a selected return period, can be expressed either in terms of an intensity measure (IM) or an engineering demand parameter (EDP). Seismic capacity, on the other hand, is defined by dividing the near‐collapse limit‐state IM or EDP by an appropriate risk‐targeted safety factor (γ _im or γ _edp ), which is the only safety factor used in the proposed decision model. Consequently, the seismic performance assessment of a building should be based on the best possible estimate. For a case study, it is shown that if the target collapse risk is set to 10^?4 (0.5% over a period of 50 years), and if the seismic demand corresponds to a return period of 475 years (10% over a period of 50 years), then it can be demonstrated that γ _im is approximately equal to 2.5 for very stiff buildings, whereas for buildings with long periods the value of γ _im can increase up to a value of approximately 5. The model using γ _edp is equal to that using γ _im only if it can be assumed that displacements, with consideration of nonlinear behavior, are equal to displacements from linear elastic analysis.     

基于植被物候特征的互花米草提取方法研究——以长三角湿地为例

互花米草自1979年引进我国以来迅速增长,呈现外来物种入侵势态,严重影响了滨海湿地生态系统平衡。通过遥感手段监测可以获取互花米草的时空扩展规律,为互花米草治理提供参考和依据。本文选取长三角地区3个主要湿地区域为研究区,依托Google Earth Engine (GEE)平台选取了2014—2019年Landsat 8 OLI时间序列数据,提出了一种基于物候特征的互花米草提取方法。首先,通过归一化差异湿度指数(NDMI)、归一化差异植被指数(NDVI)和归一化差异水体指数(NDWI)提取互花米草生长的高湿度区域;然后,通过植被指数构建表征植被物候特征的时间序列曲线,确定互花米草与其他植被的物候特征差异时相;最后,基于物候特征差异时相数据构建决策树提取互花米草。通过与现有的互花米草决策树提取方法和支持向量机(SVM)方法对比发现,本文方法在3个研究区的提取结果均为最优提取结果,表明本文的方法对提取互花米草具有较好的适用性。     

强震前地磁总场F02测值的空间相关特征研究

收集全国102个地磁台站2008年至2015年数字化地磁资料, 运用地磁空间相关法计算每日凌晨02时各台站地磁总场F之间的空间相关系数。 在使用相同的异常判别标准的情况下, 研究时段的17个中强地震中发现在2009年9月19日陕西宁强5.1级地震, 2011年11月1日四川青川5.4级地震, 2013年1月23日辽宁灯塔5.1级地震, 2013年7月22日甘肃岷县6.6级地震和2013年10月31日吉林前郭5.5级地震前均具有较为明显的空间相关低值异常现象。 通过总结5个震例的异常特征, 笔者发现其异常形态极其相似, 且平均的异常持续时间为20天, 而地震就发生在异常开始后3个月内; 同时, 地震发生在异常集中区中心附近, 且这个异常区域大小在500 km左右。 这一研究结果对于进一步分析地震前地磁空间相关异常特征积累了丰富的资料。     

环境风险应急管理决策支持系统的设计与实现

针对典型煤化工产业聚集区(太原市晋源区)环境风险应急管理决策支持系统的建立,进行系统功能结构划分和详细设计,并利用ArcGIS Server二次开发,实现在Web环境下地物空间信息的完整表达,地理信息的定位查询,应急救援资源的分类查询和对风险源的实时监控,完成最短路径查询、最邻近设施选择、救援服务区确定等空间分析功能,形成完整的应急响应机制,为应急管理决策提供有力依据。     

济南城市防汛GIS研究

首先对济南城市防汛GIS系统进行了总体设计,给出了系统的数据库设计,并重点介绍积水模拟模型的输入输出设计,然后对组件技术、积水模拟模型等系统实现的关键技术进行了阐述,最后介绍了济南城市防汛GIS的特色功能模块。系统运行良好,为济南的城市防汛提供了科学决策的依据。     

空间数据挖掘方法综述

空间数据挖掘和知识发现是空间数据库技术、空间数据荻取技术、计算机技术、网络通信技术和管理决策支持技术等发展到一定阶段的产物,是多学科相互交融和相互促进的新兴边缘学科,汇集了人工智能、模式识别、数据库、空间信息学、统计学等各学科技术的成果。如何在浩瀚的空间数据和人们的知识渴求之间建立一个桥梁的确是一个巨大的挑战,而以聚类、分类等人工智能技术为基础的空间数据挖掘为迎接这个挑战提供了新的支撑技术。总结了空间数据挖掘方法的国内外研究现状,为深入研究空间数据挖掘方法提供参考价值。     

防汛决策指挥地理信息系统构建关键问题研究

根据陕西渭河下游流域现有资料、防汛实际应用需求及未来发展趋势,对基础地理、防汛专题等空间静态数据及水雨情等实时信息进行整合处理和有效集成,并基于开放性的面向服务的B/S架构体系理念以及空间和属性数据一体化、多源数据无缝集成的建库思想,开展了融信息实时监测、三维决策支持、地图查询定位、防汛预案展示、信息采编维护等为一体的流域防汛决策指挥地理信息系统的构建以及系统架构设计、数据组织处理、数据建库优化、地图缓存配置、数据共享交换等关键问题研究,可对提高流域防汛决策管理能力、减少洪涝灾害损失及构建人水和谐环境提供有力保障。     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.