西昆仑奥依塔格石炭-二叠纪岩浆岩:弧后盆地的产物?

目前对西昆仑石炭-二叠纪火山岩分带性、形成环境、深部地幔源区特征还缺乏较好的约束。在区域地质填图、综合研究的基础上,将西昆仑石炭-二叠纪岩浆岩空间上分为南带、北带。本文展示了北带岩浆岩集中出露的奥依塔格地区玄武岩、辉绿岩、辉长岩的地球化学和Sr、Nd、Pb同位素以及与辉长岩共生的斜长花岗岩的锆石LA-ICP-MS测年数据,以约束该区基性岩形成的时代、构造环境和地幔源区特征,同时与库地玄武岩、阿羌基性火山岩进行了比较。目前的数据表明:(1)斜长花岗岩单颗粒锆石LA-ICP-MS测年得到313.6±1.6Ma、291.6±1.7Ma两组年龄,后者代表斜长花岗岩和辉长岩的侵位时代,前者可能代表玄武岩的年龄。(2)球粒陨石标准化稀土元素配分模式图中,玄武岩显示轻稀土略富集的向右缓倾模式,辉绿岩、辉长岩均为轻稀土略亏损的近平坦型;原始地幔标准化微量元素值均表现为大离子亲石元素相对富集,Nb、Ta谷明显,高场强元素中后半部分呈平坦型模式。(3)地球化学指标显示奥依塔格一带基性岩未受到或很少受到地壳物质混染,样品的Nd、Pb组成可以用来代表地幔源区的成分特点,Nd-Pb、Pb-Pb图解显示其代表的地幔源区具有"Dupal"异常,并于金沙江蛇绿岩中玄武岩代表的地幔源区有较高的一致性。(4)综合岩石地球化学、沉积组合认为奥依塔格基性岩形成于弧后盆地构造环境,区域对比,指出它与库地一些克沟组玄武岩、于田县阿羌组火山岩同为康西瓦-麻扎混杂岩带代表的洋盆向北俯冲,引发弧后盆地扩展的结果。     

台州地区地下水储量变化的CORS网反演

为了突出连续运行基准站(CORS)网对地下水储量监测能力,该文基于CORS网的高精度大地高时间序列,联合大气、土壤水和海平面变化等地表质量负荷数据,根据地球负荷形变理论,采用移去恢复技术,反演了台州地区2017年1月-2019年6月的地下水储量变化,并与GRACE-FO监测结果进行对比.结果 表明:①CORS网反演的地下水储量具有明显的季节性与年际特征,秋冬季节地下水储量较少,春夏季节地下水储量增多,年变化幅度达到1000 mm.②在空间分布上,台州中东部地区及沿海地区变化量,比西北部的内陆地区变化量大.③与GRACE结果相比,除局部区域存在差异,整体上时空变化趋势具有较高一致性.在小区域的地下水储量反演中,CORS网具有可行性.     

黑龙江多宝山地区早古生代沟-弧-盆体系重建SCIEI北大核心CSCD

沟-弧-盆体系恢复对于研究增生造山过程和解析成矿作用具有重要指示意义,多宝山岛弧带是我国重要的铜钼多金属成矿区带,其沟-弧-盆体系恢复对成矿地质背景研究及拓展区域找矿具有重要意义。通过对多宝山地区早古生代地层、岩石和构造的野外调研和系列编图,结合最新的年代学资料,重新厘定了含矿地层单位——多宝山组、铜山组的形成时代和层位:确定多宝山组形成时代为寒武纪芙蓉世-早奥陶世,铜山组形成时代为早-中奥陶世;将铜山组置于多宝山组之上,暗示地表以下保存有规模较大的主要成矿地质体——多宝山组,由此扩大了深部找矿空间。在此基础上,重建了多宝山岛弧带早古生代地层序列。在多宝山岛弧带西北侧多宝山-三卡一带划分出早古生代俯冲增生杂岩带,根据俯冲增生杂岩带与岩浆弧、伴生沉积盆地的沉积建造及时空关系,划分出弧前盆地、弧间盆地、弧后盆地等构造单元,建立了多宝山岛弧带古生代沟-弧-盆体系格架,认为多宝山岛弧带西北侧多宝山-三卡一带存在早古生代洋盆,并于奥陶纪发生南东向俯冲。指出岩浆弧靠近俯冲增生杂岩带一侧是成矿有利部位,为多宝山岛弧带铜多金属矿的成矿背景和成矿地质条件研究提供了理论依据。     

基于开源WebGIS的上海城市灾害信息系统

利用开源Web GIS架构,设计了高效率、低成本的系统建设方案,并以上海市为例,构建了上海城市灾害信息系统。系统以Map Server为核心,使用Postgre SQL作为后台数据库,具备Web信息发布、空间数据的存储与管理等基本功能,提供了地图服务,可以满足多用户并发访问,并能实现专题地图、查询统计、空间分析与建模、决策支持、制图输出等多种功能。     

中国海域有效波高、海表风速长期变化趋势的季节特征

Long-term variations in a sea surface wind speed(WS) and a significant wave height(SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation,and other activities. The seasonal characteristics of the long-term trends in China's seas WS and SWH are determined based on 24 a(1988–2011) cross-calibrated, multi-platform(CCMP) wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III(WW3) wave model forced by CCMP wind data. The results show the following.(1) For the past 24 a, the China's WS and SWH exhibit a significant increasing trend as a whole, of3.38 cm/(s·a) in the WS, 1.3 cm/a in the SWH.(2) As a whole, the increasing trend of the China's seas WS and SWH is strongest in March-April-May(MAM) and December-January-February(DJF), followed by June-July-August(JJA), and smallest in September-October-November(SON).(3) The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China's seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gulf, and the Gulf of Thailand exhibited a significant increase in all seasons.(4) The variations in China's seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.     

一种自适应时间步长的场地地表污染扩散元胞自动机仿真模型

针对传统扩散模型难以动态模拟地表污染物时空不均匀扩散过程的问题,本文提出一种基于元胞自动机模型的污染物地表扩散仿真模型,在综合考虑地表高差及粗糙度对污染物扩散过程影响的基础上,确定了不规则污染场地的元胞边界条件、划分了元胞空间、提出了一种降雨和非降雨条件下污染物扩散流速计算方法,基于分子扩散建立了地表污染扩散模型演变规则。为更好模拟地表污染物扩散情况,本文提出了一种污染物随坡度和质量衰减的元胞自适应时间步长调整算法,该算法能够动态调整元胞自动机的时间步长,防止固定时间步长在污染物快速扩散时错过细节,而缓慢扩散时消耗计算资源。实验设计了降雨和非降雨两种情形对污染物随时间扩散的过程进行仿真与分析。实验结果表明,不同下垫面对污染物扩散速度有很大的影响,污染物在糙率为0.012的水泥地表上的扩散速度约为其在糙率为0.035的一般性土壤地表上的2.7倍;降雨强度和时长能够加快污染物的扩散,且扩散速度随着降雨曲线变化而改变,并在雨强峰值附近达到最大;污染物扩散服从坡度分布特征,且随着时间变化,高污染区域范围和污染物浓度差异渐渐变小,并在一段时间后,浓度变化渐渐趋于平稳;自适应时间步长演变算法能够较好地体现一次演变过程中污染物扩散在不同邻域元胞方向上的细微时间差异,提高污染物时空分布的计算精度。     

A methodology for 3D modeling and visualization of geological objects

Geological body structure is the product of the geological evolution in the time dimension, which is presented in 3D configuration in the natural world. However, many geologists still record and process their geological data using the 2D or 1D pattern, which results in the loss of a large quantity of spatial data. One of the reasons is that the current methods have limitations on how to express underground geological objects. To analyze and interpret geological models, we present a layer data model to organize different kinds of geological datasets. The data model implemented the unification expression and storage of geological data and geometric models. In addition, it is a method for visualizing large-scaled geological datasets through building multi-resolution geological models rapidly, which can meet the demand of the operation, analysis, and interpretation of 3D geological objects. It proves that our methodology is competent for 3D modeling and self-adaptive visualization of large geological objects and it is a good way to solve the problem of integration and share of geological spatial data. Supported by National High Technology Research and Development Program of China (Grant Nos. 2006AA12Z220, 2006AA12Z114, 2007AA12Z226), and Open Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No. WKL(06)0304)     

Swarm intelligence for classification of remote sensing data

This paper proposes a new method to classify remote sensing data by using Particle Swarm Optimization (PSO). This method is to generate classification rules through simulating the behaviors of bird flocking. Optimized intervals of each band are found by particles in multi-dimension space, linked with land use types for forming classification rules. Compared with other rule induction techniques (e.g. See5.0), PSO can efficiently find optimized cut points of each band, and have good convergence in the search process. This method has been applied to the classification of remote sensing data in Panyu district of Guangzhou with satisfactory results. It can produce higher accuracy in the classification than the See5.0 decision tree model.     

四川及邻区地磁日变化异常特征研究

以四川省及其邻区31个地磁台2015年以来的垂直分量资料为研究对象,利用地磁加卸载响应比方法分析震前地磁日变化异常特征,结果显示:6组有效的地磁加卸载响应比高值异常中有5组异常对应了M5.0以上地震,异常多出现在震前6个月内;震中位于异常区边界线附近。分析认为地磁加卸载响应比高值异常是孕震区内局部地下电导率变化导致感应磁场发生改变的一种反映。     

九寨沟7.0级地震前四川地区地磁异常分析

利用地磁垂直分量日变幅逐日比方法分析了2017年8月8日九寨沟7.0级地震前的地磁异常时空变化特征,结果表明:震前6个月成都、松潘、江油和崇州4个台站出现了同步的逐日比超阈值异常,异常台站集中于四川北部地区,震中位于异常阈值线上。外空场活动调查排除了空间环境对计算结果的影响,显示此次多台同步逐日比异常很可能是浅层地下介质电性结构改变的一种表现。