多标标度法在矿产预测中的应用

在矿产预测中常涉及一些定性变量,泽这些变量的分析和应用,必须将定性描述的地质特性转化为用数值表示的变量,这就需要处理此问题的方法－－多维标度法。笔者介绍了计量性的Ｔｏｒｇｅｒｓｏｎ法、准计量性的林知已夫数量化理论和非计量性多维标度法,并列举了其在矿产预测中的应用实例。     

基于三对象多维标度法及其应用

本文提供了基于三对象（样品或变量）的多维标度法。计算方案是通过一维标度求法（数量化理论Ⅳ的扩展）和多维标度求法把原始问题降至为标准特征值—特征向量问题来解决，该方法的主要目的是提取更多信息、简化数据结构并重新标度研究对象于新的低维空间，以便能通过二维“因子轴”来直接检验，它适合于定性和定量数据。编写计算程序进行标度分析，在内蒙古赤峰某金矿进行矿产预测应用效果良好。     

基于三对象的三维标度法及其应用

本文介绍了三对象的多维标度法，计算方案是通过一维标度求法和多维标度法把原始问题降至为标准特征值－特征向量问题来解决。该方法的主要目的是提取更多信息，简化数据结构，并重新标度研究对象于新的低维空间，以便能通过二维“因子轴”来直接检验，它适合于定性和定量数据，编写计算程序进行标度分析，在内蒙古赤峰某金矿进行矿产了预测，应用效果良好。     

一种基于加权非负矩阵分解的矿产预测方法

提出了一种新颖的基于加权非负矩阵分解的矿产预测方法,运用非负矩阵分解的非负性、降维性及稀疏性对多维矿产数据进行处理。通过R型聚类分析,按照变量相似度将变量聚合成群,对相关性高的元素的聚类结果进行加权非负矩阵分解得到基向量,进行回归分析验证基向量用于矿产预测的有效性。最后,以广东省新寮岽铜多金属矿区数据为例,通过基向量预测圈定异常,绘制矿产预测分布图,得到明显的异常区域,取得了好的预测结果。     

一种异常组合分析方法及其在矿产预测中的应用

异常组合分析方法是一种矿产预测的统计方法，其特点是使用定性的多态数据，适合表现地质因素的多种异常状态。用地质变量异常组合并联性标度样品，通过典型样品与预测样品的对比分析，来进行地质异常评价和成矿预测。     

Torgerson方法在某斑岩铜钼矿蚀变与矿化分析中的应用

Torgerson方法是一种多维标度法。用于研究变量或样本对象，它从一组对象的两两之间的相似性度量或非相似性度量出发，求出这组对象在某低维空间中的标度，从而发现这组对象的整体关系。通过它在某斑岩铜钼矿的蚀变与矿化分析的实例应用，说明该方法在矿产资源评价方面具有一定的实效性。     

矿床统计预测简介 ③矿床统计预测的基本方法(上)

矿床统计预测的方法很多,哈里斯(1973)曾将矿产资源定量评价方法分为三大类:地质多元统计法、主观概率法及空间法.阿格特伯格(1974)则概括为单变量统计法及多元统计法两类,而将空间法归为单变量统计法之中.根据已有资料,这两类方法在矿产预测中均有广泛应用.本文着重介绍几种常用的单变量统计预测法,下一讲则介绍地质多元统计分析在矿产预     

改进的AHP法在煤层顶板砂岩富水性评价中的应用

针对在传统AHP法确定富水性预测评价指标权重时,采用1~9、0~2、0.1~0.9标度法构建判断矩阵过程中存在的问题,引入了新的五标度法。使得在构造判断矩阵时,更加符合专家打分心理,也避免了信息量丢失以及对原始判断矩阵进行间接的数学处理或变换。应用改进的Weber-Fechner定律评价数学模型,对葫芦素井田延安组三段3-1煤层顶板砂岩富水性进行预测评价。结果表明,改进的AHP法确定富水性预测评价指标权重时是有效的,同时也为富水性预测评价提供了一种新方法。     

非负矩阵分解基向量分析及其在矿产预测中的应用

针对矿产预测数据具有复杂性以及矿质异常信息具有稀疏性的问题,基于非负矩阵分解的非负性和降维的特点,结合稀疏性,提出一种基于NMF基向量分析的矿产预测数据处理方法,并对基向量和原变量以及基向量之间的关系进行分析。广东新寮岽铜多金属矿区数据实验结果表明,NMF方法在不同特征值和相似稀疏度的条件下的基向量形态基本稳定,在保留找矿信息的同时可有效地实现对矿产预测数据的稀疏化。NMF方法对于矿产预测具有重要的实际意义。     

辽西八家子-吴家屯多金属矿田综合信息矿产预测

随着找矿难度的日益加大和各种矿产预测理论的不断完善,应用综合信息矿产预测理论方法圈定靶区、寻找隐伏矿体越来越广泛且效果明显.在广泛收集、研究前人工作成果的基础上,重新厘定八家子-吴家屯多金属矿田综合信息找矿模型,并将其转换成地质变量,在已构置的网格单元中依据综合变量进行赋值.以GIS为平台进行空间分析和叠加,采用综合信息矿产预测的理论与方法,开展矿田的综合信息矿产预测,得到4个综合信息得分高值区（即靶区）,对矿田的进一步找矿具有一定的指导意义.     

秩特征分析方法在矿产资源预测中的应用

提出了一种新的矿产资源靶区定位预测的统计方法—秩特征分析方法。该方法以地质变量之间秩相关分析为基础,根据地质变量集合中某一地质变量与其余地质变量之间总的秩相关程度来度量该变量的重要性大小,根据每个地质变量在统计单元（万能的资源靶区）上的取值情况计算单元成矿联系度,,再根据单元成矿联系度相对大小评价优选矿产资源靶区。该统计方法可以同时使用定性、定量和半定量三种地质变量,减少了由于数据离散化而造成的地质信息丢失,可以最大限度地利用各种类型地质变量所提供的有用信息。     

地理空间数据的尺度转换

尺度一般是指空间范围的大小,地理空间数据的尺度转换是尺度研究的重要问题之一。针对地理信息系统(GIS)技术支持下的地理空间数据尺度转换问题,首先回顾了尺度转换的理论基础,即等级理论、分形理论、区域化随机变量理论、地理学第一定律等理论的基本内涵;然后总结了地理学不同研究领域内主要的尺度转换方法,重点分析了重采样法、变异函数法、分形分维法及小波分析法的基本原理、模型方法与典型应用案例;最后介绍了地理空间数据尺度转换效应研究的进展。基于上述总结和分析认为:构建一套无级变换的、系统的尺度转换方法,整合不同学科领域的数据与过程模型、形成数据模型同化的技术体系,这是地理空间数据尺度转换研究的重要课题。     

Spatial and scaling modelling for geochemical anomaly separation

A spatial and scaling approach with a user-friendly windows program is introduced which can be used to assist exploration geologists and geochemists in geochemical data analysis and anomaly separation. It can also be used for image enhancement and classification. Statistics are calculated and optimized within a variable-sized moving window centred at an arbitrary sample location. The moving window has both variable size and shape determined by three parameters: r (size), β (ratio of long and short axes), and θ (orientation). It calculates five optimal indexes for each sample location: the optimal statistic U(r₀,β₀,θ₀), optimal size r₀, shape indexes β₀ and θ₀, and scaling index α (singularity exponent). These indexes characterize the entities present in an image from different angles and, therefore, can be analyzed by means of multivariate techniques to assist in image enhancement and classification. The user-friendly program prepared can be used in conjunction with GIS (Geographic Information System) software such as ArcView to implement the spatial and scaling method. It has been applied to the stream sediment geochemical data set (923 grid samples) for gold mineral exploration in the Habahe map sheet, Altay Shan, Xinjiang, northwestern China. The spatial and scaling method provides better results than the ordinary moving average method.     

Recognizing Correlated Patterns from Remotely Sensed Images: A New Learning Rule Based on Spin Glass Theory

Classification of remotely sensed images is a rich research field wherein techniques from conventional statistics to recent developments such as Artificial Neural Network, Fuzzy logic etc. has wide applications. Conventionally remotely sensed image classification referred to pixel classification based on broad categories such as vegetation and water bodies. With the availability of high-resolution imageries, shape analysis of macro structures contained in images becomes an important and difficult task. Although conventional statistical pattern recognition techniques give a reasonable result, Artificial neural network methods seem to be giving better results. In this paper, we give a survey of feed-forward neural network used for shape classification and a Hopfield model with an improved learning rule, for a typical shape analysis problem.     

电成像测井孔隙度分析技术及其在碳酸盐岩储层产能预测中的应用

深入研究了电成像测井的测量方式,提出一种基于电成像低频分量的电阻率刻度公式,结合Archie公式并引入常规测井数据及处理成果,严格推导出一种可将电成像测井数据直接标定为孔隙度的算法。此方法省略了先作电阻率刻度再应用Archie公式等中间步骤,处理过程得以简化,并可消除Archie公式中a、b、n、S_xo、R_mf等参数以及浅侧向测井R_LLS等因素对处理结果的影响,较大程度地实现了数据的自适应性处理。在此基础上,对孔隙度频谱进行多种统计分析,开展了类似核磁的区间孔隙度分析以及类似油藏描述中渗透率评价的孔隙度径向非均质性分析,将研究成果应用于碳酸盐岩储层产能预测中,引入“孔隙贡献因子”概念,并通过测井资料和试油资料建立了“孔隙度贡献因子”与储层产油强度的定量关系。     

Anisotropic Continuous-in-Scale Universal Multifractal Cascades: Simulation,Analysis and Correction Methods

Continuous-in-scale multifractal cascades has long been an attractive choice for mathematically modeling turbulent and turbulent-like geophysical fields. These fields are usually anisotropic as they are subject to both stratification and rotation, thereby questioning the isotropy assumption often made to model them. The self-affine and generalized scale invariance approaches to scaling are used here to introduce anisotropy in such models. These anisotropic simulations have (1) unresolved large-scale features and (2) statistics that deviate from the desired power-law scaling mainly in the small scales. The former issue is solved via nesting, whereas the latter is attempted to be overcome using singularity correction methods. While earlier studies have proposed isotropic correction methods, here they have been generalized to correct anisotropic simulations. These singularity corrections seem to improve the small-scale statistical properties of mildly anisotropic simulations; nesting, on the other hand, appears to enhance statistics over almost all scales even for strongly anisotropic simulations. Both the correction and nesting techniques lead to a reduction in computational time and memory usage suggesting that nested singularity-corrected cascades offer a better framework for quantitatively modeling the atmosphere, ocean, solid earth, and associated fields.     

Numeric examination of multivariate soil samples

Numerical methods for the examination of multivariate soil samples are presented in geometric terms. Techniques of coordinate representation by principal components, by nonmetric scaling, and by a new method are discussed, as are techniques for agglomerative hierarchic cluster analysis. These are illustrated by two sets of previously published data.     

TECHNICAL NOTE: Analysis of in-situ stress measurements

In order to analyse in-situ stress measurements two approaches are discussed. These are eigen-value analysis and statistical techniques such as Arnold, Fisher and Bingham distributions. When these techniques are applied to field data, the results are quite consistent. Eigen-value analysis has the advantage of finding the mean stress magnitudes, and statistical distributions have the advantage of considering the dispersion factor.     

Wind speed prediction using statistical regression and neural network

Prediction of wind speed in the atmospheric boundary layer is important for wind energy assessment, satellite launching and aviation, etc. There are a few techniques available for wind speed prediction, which require a minimum number of input parameters. Four different statistical techniques, viz., curve fitting, Auto Regressive Integrated Moving Average Model (ARIMA), extrapolation with periodic function and Artificial Neural Networks (ANN) are employed to predict wind speed. These methods require wind speeds of previous hours as input. It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods, viz., extrapolation using periodic curve fitting and ANN and the other two methods are not very useful.