开发利用地理信息系统(GIS)综合分析地学信息进行矿产预测

众所周知,矿产资源预测是综合地学信息,进行优选靶区的有效手段之一。随着地学工作的深入和勘探技术的发展,已获取了大量的多源地学信息,如地质、地球物理、地球化学和遥感等资料。怎样从这众多的资料中提取有用信息进行综合分析,达到矿产资源预测的目的,一直是地学界探索的课题。过去,应用人工的方法来进行此项工作,不但费力、投资大,而且难以达到预期的效果,方法技术也不利于推广。如今,高速、高质量的计算机已趋于普及,使应用计算机技术高效地处理堆积如山的资料成为可能,尤其是近些年发展起来的地理信息系统(GIS),为综合处理地学资料的矿产资源预测的方法技术,开拓了广阔的前景。地理信息系统是一种计算机系统的应用软件,它集数据库管理和图像分析技术为一体,以空间数据迭加分析为重要特征。地学工作者进行矿产资源预测时,通常是从多源地学资料中提取有用信息,再应用专家知识,结合数学方法,如逻辑运算、贝叶斯(Bayes)规则等,建立预测模型,以模型的推理网格为线索,利用地理信息系统有效的空间分析手段,把各种证据图层综合迭加,最终产生以概率为指标的矿产资源预测图,高概率指示有利的矿产资源远景区,可作为勘探者和决策者进行勘查规划的依据。     

通渝隧道围岩变形的神经网络预测

隧道新奥法施工中 ,常以围岩变形量作为评判围岩稳定性和支护结构经济合理性的重要指标。公路隧道围岩变形量是随时间而变化的数据序列 ,因而可以建立一些实时跟踪预测模型和方法。根据通渝隧道围岩拱顶下沉位移变形的特性 ,采用神经网络技术来预测其变形量 ,结果表明该方法简易、有效     

基于GIS的攀西地区铂族元素成矿地质条件初步分析

攀西地区以良好的铂族元素(PGE)成矿地质背景，而成为我国铂族元素找矿热点地区之一。本文在建立攀西地区铂族元素矿床地质概念模型的基础上，用地理信息系统(GIS)分析了该地区的铂族元素成矿地质条件，包括地质构造、峨眉山玄武岩、基性一超基性岩和铂族元素地球化学异常特征。指出研究区的深大断裂对铂族元素的富集表现出很好的控制作用，峨眉山玄武岩为铂族元素的矿源层之一，岩浆岩为铂族元素的富集提供了成矿物质及成矿能量。     

Electron microscopy study of the sakhaite from endogenous borate deposit in Hunan, China

Sakhaite was first discovered by Ostrovskaya, Pertsev and Nikitina at Siberia in the former Soviet Union. By using the X-ray diffraction technique, they proved that the crystal system of sakhaite was cubic (a = 1.464 nm), its possible space groups were Fm3m, F432, F43m, Fm3, F23, and its crystal chemical for-mula was Ca48Mg16(CO3)16(BO3)28Cl4(OH)8 4H2O[1]. Chichagov, Simonov and Belov studied the crystal structure of synthetic sakhaite and determined that its space group was F4132, …     

人工神经网络在岩体质量分级中的应用

结合四川省金沙江某水电站工程实例，应用BP人工神经网络方法建立3层BP网络模型，选取岩石单轴抗压强度等6个影响因素为输入变量，对坝基复杂岩体进行质量分级。通过机算机Visual C 语言编程实现神经网络模型，进行网络的学习和运算。以神经网络合理结构分析方法选取合理结构，确定合理隐层单元的数量，提高网络测试的精度。对测试结果的分析发现，经过优化的BP网络模型经多次学习后，测试精度提高，结果可靠，取得较好的实际应用效果。     

昌马水库右岸山体稳定性研究

昌马水库导流排砂洞塌方导致右岸山体发生松动 ,本文在大量地质资料的基础上 ,对右岸岩体裂隙网络进行模拟 ,同时 ,采用极限分析能量法和有限元数值模拟方法等对右岸山体的稳定性进行分析研究 ,并提出相应的加固方案。     

云南武定迤腊厂铜矿含矿石英脉40 Ar-39Ar年龄及其意义

对武定迤腊厂铜矿成矿期石英进行了40Ar-39Ar同位素年龄测定,得到马鞍形年龄谱,坪年龄为(784.25±0.95)Ma,等时线年龄为(783.93±8.59)Ma.地质特征研究表明该矿床后期改造作用明显,并非同生沉积或成岩作用早期成矿,而与晋宁期Rodina大陆裂解有关.武定迤腊厂铜矿的形成可能是在Rodinia大陆裂解时,从深部带来大量成矿物质,改造成岩时期初始的矿化,形成矿床的叠加富集和最终定位,晋宁-澄江期是该矿床的主成矿期.     

东川桃园式铜矿Ar-Ar同位素年龄及意义

通过对东川桃园铜矿与铜矿共生石英的40Ar/39Ar同位素年龄的测定,得到马鞍形年龄谱,其坪年龄为768.43Ma±0.58Ma,等时线年龄为770.00Ma±5.44Ma。该矿床后期改造作用明显,并非同生沉积或成岩作用早期成矿,而与晋宁期Rodina大陆裂解有关。东川铜矿的形成可能是在Rodinia大陆裂解时,从深部带来大量成矿物质改造成岩时期初始的矿化,形成矿床的叠加富集和最终定位,因此,晋宁-澄江期是东川铜矿的主成矿期。     

On the long-term seismic hazard analysis in the Zhangjiakou–Penglai seismotectonic zone, China

The Zhangjiakou–Penglai seismotectonic zone (ZPSZ) lies in the northern part of North China and extends along the Zhangjiakou–Beijing–Tianjin–Bohai Bay–Penglai–Yellow Sea. It is about 900 km long and some 250 km wide in a northwest direction. The great Sanhe-Pinggu (M_S=8.0) earthquake occurred on September 1679 and the Tangshan (M_S=7.8) earthquake on July 1976 caused serious economic and life losses. According to some differences in crust structure and regional tectonic stress field, the ZPSZ is divided into western and eastern segment by the 117°E line for study on long-term seismic hazard analysis. An analysis of Gutenberg–Richter's empirical relation of earthquake-frequency and time process of historic and recent earthquakes along the eastern and western segments shows that the earthquake activity obeys a Poisson process, and these calculations indicate that the earthquake occurrence probability of M_S=6.0–6.9 is 0.77–0.83 in the eastern segment and the earthquake occurrence probability of M_S=7.0–7.9 is 0.78–0.80 in the western segment of the ZPSZ during a period from 2005 to 2015.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.