撞击坑识别方法综述

目前国内外有多种撞击坑识别方法,在一定程度上实现了对撞击坑的识别提取,但是其准确性以及对数据的适应性不尽相同。首先对撞击坑识别研究进展进行概述,再对撞击坑识别方法进行归纳总结,指出不同方法的优缺点和适用条件。最后,对撞击坑识别研究存在的问题进行分析,提出了研究撞击坑识别的重点及解决途径。     

基于最小二乘支持向量机的城市桥梁震害预测模型研究

针对地震中城市桥梁震害状态具有较强的非线性、复杂性的特点,采用了具有RBF核函数的最小二乘支持向量机(LS-SVM)算法。在大量收集我国地震中城市桥梁震害资料的基础上,将此算法引入桥梁的震害预测中,选取了地震烈度、上部结构、地基失效程度、支座类型、墩台高度、桥梁跨数和场地类别等因素作为模型的特征输入向量,建立了最小二乘支持向量机的桥梁震害预测模型。通过反复地样本训练及模型参数设置,仿真结果表明,该方法具有一定的准确度和可行性。基于最小二乘支持向量机的桥梁震害预测方法是一种可以用于地震中桥梁震害预测的良好方法。     

The discovery of Neoproterozoic extensional structures and its significance for gas exploration in the Central Sichuan Block,Sichuan Basin,South China

The Central Sichuan Block(CSB) is the hardest block between the deep faults of Pujiang-Bazhong and Huaying Mountain in the central part of Sichuan Basin, which lies in the northwestern part of the upper Yangtze Craton. The CSB has long been considered as the oldest and most stable core area of Yangtze Craton, with the uniform basement and high level of hardening. Here we present a detailed interpretation of deep structures in the CSB by integrating high-resolution seismic data(approx. 50000 km2) with large-scale aeromagnetic data. Results show that eight Neoproterozoic extensional structures of different scales are nearly EW-, NEE-, and NW-trending in the CSB. Discovery of these extensional structures changes previous understanding of the CSB as a unified block. The extensional structures experienced one or two stages of extension in the longitudinal section, and filled with 3000–5000-m-thick weakly magnetic materials. Development of basal A-type granite in Weiyuan, Sichuan Basin and bimodal volcanic rocks of the Suxiong Formation, Western Sichuan confirms the CSB's Neoproterozoic extensional tectonic setting. The newly discovered Neoproterozoic extensional structures are of great significance for source rock and favorable sedimentary facies distribution, reservoir development, and gas accumulation.     

基于PCA-GASVM的晋陕甘宁地区生态环境评价

我国新型城镇化进程中产生较严重的生态环境问题。选取晋陕甘宁地区为研究对象,从城镇生态压力、状态、响应3个方面构建了生态环境的评价指标体系,并通过2013年中国统计年鉴整理到相关数据,利用PCA-GA-SVM对晋陕甘宁地区生态环境进行评价。结果表明:PCA-GASVM生态环境评价比GASVM评价具有更高的准确率,而且PCA-GASVM比GASVM评价收敛速度更快。甘肃、宁夏的自然保护区占辖区面积比重指标排名比山西、陕西两个省名次靠前,从森林覆盖率分析只有陕西位于第11位,山西、甘肃、宁夏森林覆盖率均位于后20名;从SO₂排放量、烟尘(粉尘)排放量、工业废弃物产生量、废水排放量上分析,在晋陕甘宁地区中,宁夏最优,甘肃次之,陕西、山西排放很严重。晋陕甘宁地区生态环境保护应在"新常态"下要有"新状态",将市场机制与政府的补贴、税收等政策宏观调控相结合,严守生态红线,发展生态经济绿色科技、清洁生产,减少工业废弃物排放,建立环保交易市场,推行污染第三方治理,完善生态补偿制度,走集约、智能、绿色、低碳的新型城镇化道路。     

新疆西昆仑硝尔库勒大型锑矿的发现及其意义

硝尔库勒大型锑矿是在最近1∶5万区域地质矿产调查中发现的,经过进一步的普查工作,已查明(333)+(334)类锑金属资源量达20.36万吨,平均品位8.90%,其规模居昆仑-秦岭锑成矿带之首,是近年来西昆仑地区的重大找矿突破。矿床赋存于中二叠统黄羊岭组二段(P2h2)碎屑岩中,受NE向层间破碎带控制。矿体呈脉状、似层状、透镜状产出,在走向和倾向上均呈舒缓波状延伸,具尖灭再现、膨大狭缩、分支复合特征。矿石矿物主要为辉锑矿,次为少量黄铁矿,共生矿物为少量雄黄和雌黄,次生矿物为少量锑华及褐铁矿,脉石矿物主要为石英,次为长石、方解石等。矿石自然类型主要为硫化矿石,其工业类型为单一锑矿石,矿床成因上属层控构造改造型低温热液矿床。硝尔库勒锑矿的发现,进一步说明了西昆仑矿集区具有相应层位寻找锑矿的巨大潜力。     

西藏喜马拉雅成矿带错那洞超大型铍锡钨多金属矿床的发现及意义

经过地质调查工作在西藏喜马拉雅成矿带东段扎西康矿集区南部新识别出由花岗片麻岩、强变形片岩和弱变质板岩组成的错那洞大型片麻岩穹隆构造系统。穹隆核部侵入的淡色花岗岩主体为含电气石和石榴子石的二云母花岗岩,具多期侵入的特征,其中与成矿有关的中细粒电气石二云母花岗岩的锆石U-Pb年龄为(16.64±0.38)Ma。在岩体边部强变形的片岩带中新发现了环穹隆展布,层位稳定的矽卡岩带和厚大的铍多金属工业矿体。预查工作通过稀疏地表工程控制,证实铍达到超大型规模,共生的锡、钨达到大型矿床规模,揭示出西藏喜马拉雅成矿带除原有的铅锌、金锑等矿种外,铍锡钨多金属还具有巨大的找矿潜力。该首次发现和报道的超大型铍锡钨多金属矿床对再认识喜马拉雅成矿带新生代构造-岩浆演化与成矿作用,指导区域找矿具有重要的意义。     

地理数据服务语义表达与自动发现

地理数据的复杂性和不支持语义的服务发现机制常常导致数据用户很难准确地找到自己真正需要的地理数据服务。为了便于用户从语义上快速准确地找到自己需要的地理数据服务,本文首先构建了地理元数据本体,并采用元数据本体来描述地理数据服务的相关语义信息,接着提出了基于本体的概念间语义相似度计算的算法,最后在该算法的基础上,通过语义匹配来实现地理数据服务基于语义的自动发现。实验结果表明,文中的方法可以提高地理数据服务发现的准确率和智能化程度。     

似大地水准面的支持向量机模型研究

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有较好泛化性能的回归方法。本文简要介绍了SVM原理,针对大面积复杂似大地水准面的确定问题,仅依据测区的GPS水准实测数据,利用SVM方法整体建模。通过工程实例并与神经网络模型进行对比,证实了SVM似大地水准面模型的可靠性。     

基于神经网络知识发现的GIS决策支持系统

在分析人工神经网络结构及功能的基础上,探讨以径向基函数(RBF)神经网络作为分析推理核心的GIS决策支持方法,重点研究了RBF网络的学习算法,并在此基础上建立了可对复杂非线性数据进行知识发现的四库三功能GIS决策支持系统。     

Flooding and its relationship with land cover change,population growth,and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.