地理信息系统技术在中小学布局调整中的作用

我国“十五”期间开始中小学布局调整,经过几年的调整,教育条件、教育质量都有所改善,但也带来一些问题,这些问题的根本原因在于缺乏科学预测和规划。地理信息系统技术是利用计算机解决各学科空间问题的学科,它可以在中小学布局中进行科学分析和预测,为中小学布局调整提供决策支持。     

城市居住单元环境质量的高分辨率遥感评价方法研究

高分辨率遥感影像由于其较高的空间分辨率使得在居住单元的尺度上进行城市环境质量的遥感评价成为可能。所研究的评价方法在选取传统的环境评价指标如绿地景观结构参数、建筑容积率参数基础上,提出了平均可视面积、影像地物交替复杂度以及山体、水体、绿地、道路等外界地物影响力等指标,进一步研究了相关指标的分析方法。最后采用模糊评判的方法得到各参数指标的评判矩阵,进一步获得各居住单元综合指标评价的最终结果。使用厦门市QuickBird遥感影像进行了相关评价方法的实验,对8个典型居住单元的环境评价实验研究表明,利用高分辨率影像进行城市人居环境质量评价是一种成本较低、简便可行的方法。     

基于BSR的AVO正演估算水合物含量方法的研究

水合物地震属性研究的一个基本目标是水合物/游离气含量的估算. 这项工作的难度体现在地震反演具有多解性. 这项工作涉及到地震数据的精细处理、速度分析和BSR界面AVO分析等多个具体环节. 本文继承前人的有关成果,尝试进行了水合物/游离气含量估算方法的研究. 以区域地质、地震和化探等多元方法信息为基础,以定性推断BSR以及BSR界面AVO性质为导向,通过AVO正演模型方法,半定量（或定量）地估算BSR界面上与下地层中水合物/游离气（或水合物/水合物）的含量. 运用这种方法,结合海上有利于天然气水合物的E研究区某测线地震资料,尝试估算了BSR界面之上和之下介质中水合物/游离气的含量.     

无单元法用于地震波波动方程模拟与成像

波动方程方法是解决地震正反演问题的基本工具之一.无单元法作为一种新兴的偏微分方程数值计算方法,已经在材料力学、热传导等领域取得了显著的成功.由于抛弃了单元的概念及采用滑动最小二乘的拟合方法,使得无单元法具有前处理简单、精度高、独立变量解高次连续等优点.本文首先介绍无单元法求解波动方程的原理,指出影响其精度的主要因素.在算例的基础上详细讨论了无单元法用于实际波动问题的效果,并进一步尝试利用无单元法进行地震波数值模拟和反演成像的研究.模型计算的结果表明,无单元法能够较好的处理地震模拟和成像问题,精度和稳定性是令人满意的.     

印度洋9.0级大地震激发的地球球型振荡和环型振荡

综合分析了中国数字地震台网（CDSN）改造后的5个长周期地震仪台站观测的3天的VHZ、VHE和VHN波形资料,利用功率谱密度估计方法,在没有对资料进行去固体潮处理的情况下,准确获得了2004年12月26日印度洋地震激发的0S3~0S78的基频球型振荡和部分谐频球型振荡和0T3~0T67的基频环型振荡,并与地球初步参考模型（PREM）的理论自由振荡周期进行了对比,发现实测振荡周期与PREM预测的振荡周期符合的很好.频率与PREM模型略微不一致的球型或环型振荡可以解释为地球介质的横向不均匀性和各向异性所致.因此地球自由振荡信息可用于揭示地球的三维不均匀结构信息或各向异性信息,并可能对区分地幔对流模式有所帮助.     

断层泥中石英碎砾溶蚀形貌的测年研究

考虑到同一条断层的同一个位置所取的断层泥中,不同碎砾的溶蚀形貌并不相同,即使是同一个碎砾,其不同的表面,溶蚀形貌也可能不同,将统计学参数估计的方法引入结果处理中,并把在金川水电站坝区F1断层一次所取得断层泥样品碎砾的各个表面的总体当作一个整体进行处理,得出此断层的活动时间可能为早更新世的结果。与ESR法测得的结果基本一致。     

瑞雷波技术在工程场地勘探与评价中的应用

瑞雷波法是近年发展起来的一项新兴工程物探方法。因具有速度快、成本低、无需钻孔且分率较高等特点,在工程场地勘探与评价中有广泛用途。基于瑞雷波在弹性介质中的传播理论,阐述了瑞雷波在工程场地勘探与评价中的现场测试技术和室内资料整理方法,并结合工程实例,分析评价瑞雷波技术在工程场地中的勘探与评价效果。结果表明,瑞雷波技术能有效划分工程场地地层和正确评价工程场地类型。     

略谈矿产勘查新规范中存在的几个问题

在几年的工作实践中发现矿产勘查新规范存在一些问题,主要是量化度不够、操作性较差、关键词语多解或模糊,特别是在地质普查间距、储量估算方面不易理解掌握,造成地质工作的普遍简单化.     

Decumbent development: Urban sprawl in the Guwahati Metropolitan Area,India

Urban sprawl has become a global phenomenon as an outcome of growing population and rapid urbanization. Previous studies have addressed the rising incidence of uncontrollable urban development, particularly in peri-urban areas of cities, leading to chronic urban sprawl. The city of Guwahati, a million city in north east India, has expanded significantly in recent years. In this article, the links between population and growth of built-up areas were examined using geo-spatial techniques and remotely sensed datasets. The results indicate that the sprawl has accentuated in recent years. The intensity of land use remained uneven due to marked variations in the distribution of built-up areas, plausibly an outcome of unplanned urban growth. If current trends are anything to go by, future urban sprawl could pose serious threats to the vulnerable eco-sensitive and peri-urban areas of Guwahati. Secondary cities have unfortunately received scant attention in urban policy research, and Guwahati, epitomizes urban woes in a developing country.     

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.