一种基于图层综合的矿产资源潜力制图模型

提出了一种基于图层综合的矿产资源潜力自动制图模型。应用该模型生成矿产资源潜力分布图分三步完成:第一步,以每一种找矿标志的空间分布图为依据,生成相应的基本概率分配函数栅格图;第二步,统计综合基本概率分配函数栅格图;第三步,生成研究区矿产资源潜力分布图。利用新疆北部多拉纳萨依—阿舍勒地区的地质资料,比较了该模型与合成有矿可信度模型的找矿靶区圈定结果。两种模型的靶区圈定结果基本相同,证明证据理论模型是有效的和实用的。     

一种基于GIS的矿产资源潜力评价的自动制图模型

在基于GIS技术的矿产资源评价工作中,矿产资源潜力评价的自动制图模型通常用来统计综合多源地学信息以便自动圈定成矿远景靶区。在本文中,笔者以人工智能研究领域中的一种不确定推理模型——确定性理论为基础,提出了一种新的矿产资源潜力评价的自动制图模型——合成有矿可信度模型。该模型可以根据研究区各种成矿有利和不利证据的空间分布图,统计生成对应于每一种证据的有矿可信度栅格图,然后,按照特定的有矿可信度合成规则,将所有的有矿可信度栅格图统计综合生成合成有矿可信度栅格图。以该图为依据,可以把研究区内合成有矿可信度相对较高的成矿远景区圈定出来。也可以生成研究区合成有矿可信度等值线图。应用该模型预测了新疆北部多拉纳萨依—阿舍勒地区的多金属成矿远景,并将预测结果与证据加权模型预测结果进行了比较,两种模型的预测结果基本相似,证明了该模型的有效性和实用性。     

一种基于GIS的矿产资源的潜力评价的自动制图模型

在基于GIS技术的矿产资源评价工作中，矿产资源潜力评价的自动制图模型通常用来统计综合多源地学信息以便自动圈定成矿远景靶区。在本文中，笔者以人工智能研究领域中的一种不确定推理模型－确定性理论为基础，提出了一种新的矿产资源潜力评价的自动制图模型－合成有矿可信度模型。该模型可以根据研究区各种成矿有利和不利证据的空间分布图，统计生成对应于每一种证据的有矿可信度栅格图。然后，按照特定的有矿可信度合成规则，将所有的有矿可度栅格图统计综合生成合成有矿可信度栅格图。以该图为依据，可以把研究区内合成有矿可信度相对较高的成矿远景区圈定出来。也可以生成研究区合成有矿可信度等值线图。应用该模型预测了新疆北部多拉纳萨依－阿舍勒地区的多金属臧放远景，并将预测结果与证据加权模型预测结果进行了比较，两种模型的预测的结果基本相似， 证明了该模型的有效性和实用性。     

奇异性理论在个旧锡铜矿产资源预测中的应用: 成矿弱信息提取和复合信息分解

本研究的目的是应用非线性理论和高新信息处理技术获取矿产资源预测综合信息, 开展以有色金属和贵金属矿产资源潜力评价和预测靶区圈定, 提交个旧及周边地区矿产资源潜力分布图.围绕该研究任务, 重点开展了如何应用奇异性理论和方法, 对比个旧东西矿区的异同.由于区域构造和岩体分布等空间变化性, 导致东西区成矿背景存在较大差异, 受出露地表或近地表矿体分布和矿山开采的影响, 东西区的成矿异常强度和大小都存在较大差异, 东区总体呈高背景而西区为低背景, 因而, 对东西区的成矿信息对比研究和异常圈定相对困难.采用局部奇异性分析方法从地球化学分形密度的角度圈定了局部异常, 在东西区均较好地反映了致矿地球化学异常的分布, 同时采用广义自相似分析方法分解了综合地球化学异常和背景.结果表明, 东西区地球化学背景差异悬殊, 而局部异常具有显著的自相似性.据此在东西区同时圈定的局部异常具有内在的相似性和表现形式上的多样性, 以此为依据所圈定的靶区均具有找矿意义.      

GIS空间分析在成矿预测中的应用——以西天山为例

GIS作为一种先进技术,以其强大的制图和空间分析功能,在地学领域中得到了广泛应用.以成矿预测理论为基础,用GIS空间分析进行成矿预测并分为三步加以阐述:数据的采集与建库、GIS成矿预测空间模型的建立、预测靶区的圈定,重点介绍了建立成矿预测空间模型中的地质异常与找矿有利度分析.对西天山北部金、铜矿点进行了空间分析,探讨了在GIS平台上开展区域成矿规律及成矿预测研究的方法.     

豫西南杜关—云阳钼多金属成矿预测研究

选取豫西南杜关—云阳钼多金属成矿带为研究区,利用多学科资料,使用非线性方法提取了多元、多维、多尺度、多类型找矿信息。在应用层次分析法集成多元信息的基础上,进一步用"Kriging+Natural Breaks"和C-A分形法分别圈定了找矿靶区。结果表明,两者所圈定的找矿靶区基本一致,但后者在局部地段能够更细致地刻画靶区的细节,将找矿靶区进一步划分为Ⅰ、Ⅱ、Ⅲ级。其中Ⅰ、Ⅱ级预测区内包含有已知矿床,Ⅲ级预测区具有较好的找矿潜力。该成果为本地区的下一步找矿工作提供了依据。     

“证据权”法在西南“三江”南段矿产资源评价中的应用

证据权法本身是一种离散的多元统计方法。阐述了矿物资源潜力区的概念,简述证据权法的基本原理。以西南“三江”南段为例,首先在地质、地球物理、地球化学以及遥感地质等致矿信息提取的基础上,运用证据权法定量分析各类致矿信息(控矿因素)与矿产资源空间分布的关系;进而选择最佳控矿因素作为资源评价变量,据其变量计算每个单元的后验成矿概率;最终根据后验成矿概率定量圈定和评价找矿靶区。实践证明:在GIS环境下“,证据权”与找矿信息结合能够有效区分矿化有利地段和不利地段,从而达到定量快速圈定和评价找矿靶区之目的。整个过程是借助于MOPAS3.0评价系统实现的。     

地球化学组合求异方法在吉林省和龙南部地区找矿中的应用

玄武岩覆盖区找矿一直是矿产资源勘察中的热点与难点,而地球化学求异则是覆盖区找矿研究的重中之重。本文应用地球化学元素空间定量组合求异理论,对和龙地区地球化学元素数据进行处理与分析,求出该地区组合元素空间分布信息。并结合相关地质特征,阐明该区地球化学元素空间分布特征及与成矿的关系,为该区矿产资源勘查靶区的圈定提供重要依据。     

青海北巴颜喀拉成矿带基于专家证据权重法锑金矿资源潜力评价

青海北巴颜喀拉成矿带虽然总体研究程度低,但显示出巨大的找矿潜力,急需寻求新的找矿突破。利用前人的地质、矿产、化探等资料,建立了青海北巴颜喀拉成矿带基于GIS的空间数据库。在此基础上重新建立了研究区锑金矿区域找矿模型,其中主要找矿标志包括金水系沉积物异常、锑水系沉积物异常、砷水系沉积物异常、重砂异常、NW－NWW向线性构造和巴颜喀拉山群。采用作者提出的专家证据权重法,对研究区首次进行了基于GIS下的专家证据权重法矿产资源评价工作。据此重新圈定找矿远景区和找矿靶区,将后验概率大于0.001的区域作为找矿远景区,将后验概率大于0.005的区域作为找矿靶区;并依据找矿靶区内预测含矿区域后验概率二重积分大小,将找矿靶区的找矿潜力级别由高到低分别划分为A 、B 、C 3级,其中A级6处,B级10处,C级13处。     

西昆仑昆盖山北坡硫铁矿地质特征与找矿远景

昆盖山北坡区域矿产资源丰富,以火山岩型铜矿、铁矿、黄铁矿最具有工业价值,其次多为砂金和石膏矿等。通过在该地区以寻找海相火山岩型硫铁矿为主攻目标的工件,分析研究了该地区主要地层、岩石与成矿的时空关系和主要控矿构造空间形态及其变化;了解工作区矿石物质组分、矿石结构构造特征和矿石类型。通过研究,大致查明了工作区矿体数量、分布范围等,并圈定出成矿潜力较好,进一步勘查有重大突破的重点工作区三处,找矿靶区两处。     

矿床模型综合地质信息预测资源量的估算方法

未发现矿产资源潜力的资源定量估算一直是资源评价的重要工作,关于资源量估算的方法也在不断的探索中。从新一轮全国重要矿产资源潜力评价的技术体系所倡导的成矿分析方法入手,探索了资源量估算的成矿地质体体积法,介绍了该方法的原理、步骤和可能实现的途径。     

岩溶水系统的径向基神经网络仿真

岩溶水系统的复杂性决定了其输入与输出间具有非常复杂的非线性关系,利用人工神经网络方法进行系统的仿真是一种十分有效的手段。本文以MATLAB为平台,介绍了RBF网络的基本原理与训练方法,具有结构自适应确定、输出不依赖初始权值的优良特性。试用该方法建立了济南市岩溶水系统地下水位及其影响因子间的RBF网络模型,讨论了训练样本集与检测样本集的构建、原始数据的预处理方法、神经网络训练误差设置等重要环节,并与同结构的BP网络进行了对比,其结果BP网络效果依赖初始权值,表现出极不稳定性,且训练速度更慢,RBF网络具有更好的应用价值。     

Indicator Pattern Combination for Mineral Resource Potential Mapping with the General C-F Model

As an uncertain reasoning model, the general C-F model was originally developed for processing the uncertainties of rule-based knowledge in the field of artificial intelligence. In this model, certainty factors and combined certainty factors are defined and used for expressing the strengths of knowledge rules and knowledge rule combinations, respectively. The certainty factor can reflect the believable degree of inferring hypothesis on the basis of a proof. Similarly, the combined certainty factor can reflect the believable degree of inferring hypothesis on the basis of the proof combination. It is a function of the related certainty factors and can be determined through combining the certainty factors via the combining rule of the general C-F model. In this paper, the general C-F model has been successfully applied to mineral resource potential mapping. We call this model as the applied form of the general C-F model. In this applied form, the certainty factor is applied to expressing the believable degree of inferring mineral occurrence on the basis of one of the map pattern states associated with the mineral occurrence. Correspondingly, the combined certainty factor is applied to expressing the believable degree of inferring the mineral occurrence on the basis of the map pattern state association. And it is also applied to expressing mineral resource potentials in the mineral resource potential mapping. In the current form, the first step in implementing the general C-F model is to estimate a pair of certainty factors for each map pattern under combination. The next step is to determine the combined certainty factor for the map pattern states coexisting in each locality of the mapping area. The last step is to generate the combined-certainty-factor raster map or the combined-certainty-factor contour map in order to select mineral resource targets. The applied form of the general C-F model is demonstrated on a case study to select mineral resource targets. The experimental results manifest that the model can be compared with the weights-of-evidence model in the effectiveness of mineral resource target selection.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

中国矿产资源潜力评价集成数据库模型

提要：中国矿产资源潜力评价专项研究范围涵盖成矿地质背景、成矿规律、矿产预测、重力、磁测、化探、遥感、自然重砂等专业领域,已形成了海量成果,数据量达TB级。研制与实现中国矿产资源潜力评价集成数据库模型,用于集成和管理这个国家基础性数据资源,是一项极具挑战性的任务;是构建矿产资源潜力评价技术平台的基础工作,用以整合基础数据与高层应用、贯通省级-大区-全国层次以及支撑不同尺度规模矿产资源潜力评价常态化工作。对开展国家基础性数据资源建设有指导作用和实际意义。     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

BP和RBF在断层防水煤柱留设宽度预测中的应用

在总结全国各典型煤矿断层防水煤柱相关资料的基础上,以水头压力、煤层厚度、安全系数、煤的抗张强度为主要影响因子,选择有代表性的样本数据,通过Matlab软件构建了BP和RBF神经网络模型,对各煤矿断层防水煤柱的留设宽度进行了预测,并与规程经验公式计算的结果进行了对比。结果显示,在煤矿断层防水煤柱留设宽度预测中,RBF神经网络比BP神经网络的训练速度更快,效率更高,具有更加广阔的应用前景。      

基于GIS的矿产资源评价信息系统分析与设计

在MapGIS的基础上对矿产资源评价系统进行系统需求研究,总结出矿产资源评价信息系统的总体结构,给出了矿产资源评价中经常用来解决资源量的定位问题的数学模型——矿床定位预测特征分析模型,并对矿产资源评价系统进行了结构设计。