共查询到19条相似文献，搜索用时 171 毫秒
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秩相关权法及其在资源靶区定位预测中的应用 总被引：2，自引：0，他引：2
提出了一种矿产资源靶区定位预测的统计方法－秩相关权法。该方法以评价目标变量（矿床的资源量，矿床规模等）与成矿和控矿地质变量（定量地质变量，半定量地质变量和定性地质变量）之间的秩相关性研究为基础，用地质变量与目标定量之间的τ秩相关系数来衡量地质变量的重要程度，用矿床统计单元之间的联系度来评价资源靶区的优劣。 相似文献
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提出了一种矿产资源靶区定位预测的统计方法秩相关权法。该方法以评价目标变量（矿床的资源量、矿床规模等）与成矿和控矿地质变量（定量地质变量、半定量地质变量和定性地质变量）之间的秩相关性研究为基础，用地质变量与目标变量之间的τ秩相关系数来衡量地质变量的重要程度，用矿床统计单元之间的联系度来评价资源靶区的优劣 相似文献
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本文通过结合信息熵和灰色关联度来分析地质变量和矿床单元之间的关系,以达到圈定成矿靶区的目的。在地质、
物探、化探、重砂等资料研究的基础上提取了87个综合信息地质变量。在综合信息矿产预测理论与方法的指导下,将研究
区域划分为16个金矿床密集单元和14个金异常密集区为单元,引入熵权法对定性变量和定量变量分别计算信息熵和变量权
值,然后采用灰色关联分析法来定量解析单元之间的关联程度。结果表明：重力梯度、航磁、重磁吻合以及汇水盆地异常
等地质变量可以作为研究区域寻找金矿的有效地质标志,6号、 22 号、 18号和19号等单元可以作为一级远景成矿靶区,这与
很多相关研究结果基本一致,从而表明该方法能被有效应用于圈定找矿靶区。 相似文献
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“证据权”法在西南“三江”南段矿产资源评价中的应用 总被引：3，自引：5，他引：3
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。 相似文献
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矿产资源体的形成受一定的成矿条件和控矿因素约束．这些约束条件可以分为成矿必要条件和成矿充分条件两大类．必要条件决定了资源体能否形成,充分条件决定了资源体的具体经济指标．利用某个成矿必要条件可把研究区内的所有单元分成“可能成矿单元”和“非成矿单元”两大部分．通过多个成矿必要条件的逐步筛选,又可从“可能成矿单元”中进一步提取“成矿单元”,从而实现矿产资源的定性预测．把所有单元通过如上所述的筛选过程并提取“成矿单元”的方法称为“筛选法”（ｓｉｆｔｉｎｇｓｃｈｅｍｅ）．在模型矿床存在的条件下,可通过充分条件在地质变量和资源量之间进行数字建模,并对未知“成矿单元”进行定量评价．研究表明,筛选法有可能为采矿企业提供更可靠的找矿靶区． 相似文献
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针对矿产资源预测中的预测区与已知区信息不对称问题,提出了一种限定作用域的矿产资源量综合信息定量预测方法,并将该方法应用于桂西一滇东南地区的锰矿预测。其主要步骤是:①地质变量的作用域为数据范围、作用范围和在已知区内的值域范围三者的交集,避免了当原始数据的范围未覆盖整个预测区时对数据进行外推的操作;②预测模型的作用域为其包含的全部地质变量的作用范围的交集,当构成模型的地质变量的作用域的交集为空时,模型是无意义的;③通过地质变量组合,采用多元线性回归方法在已知区构建出一系列定量预测模型;④对于每个预测单元,从一系列定量预测模型中选取地质信息量大、线性回归效果好的模型,用于预测其单元资源量。该方法预测过程是预测区的成矿作用要素与在已知区建立的预测模型之间进行匹配的过程,充分保证了预测区与已知区的信息的对等性。 相似文献
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结构方程模型是一种建立、估计和检验因果关系的方法。它可以替代多重回归、路径分析、因子分析、协方差分析等方法,清晰分析单项指标对总体的作用和单项指标间的相互关系,是一种主要应用于验证性模型分析的多元统计建模技术。由于能够通过可观测变量来度量潜变量得分以及分析不同子模型下潜变量之间的协同效应等优点,结构方程模型被广泛应用在心理学、行为学、市场学等领域的数据建模分析研究中,提供了从提出概念—设计模型—获取数据—验证模型的成熟应用路径。地学数据的建模技术一直是地学研究的热点之一,其目的是在海量、多元、高维、多时态的地学数据中,提取出有价值的模型结构以及潜变量,研究不同地学变量以及潜变量之间的交互关系,从而支撑环境治理、灾害防治、资源勘察、生态评价等相关应用和研究。随着地学数据规模变化和建模工具的不断发展,地学数据建模的样本逐渐从抽样建模变为全样本建模,建模方式从有地学模型指导下的建模变为无约束/弱约束建模,建模依据从基于变量因果关系建模变为基于变量相关性的建模,模型复杂度从单模型/单过程建模变为多模型/多过程的综合建模。结构方程模型作为一种综合的建模方法,其可以同时包含因子分析、潜变量估计、路径分析等多种多元分析技术,这种多层次、多分支的建模方法融合了知识驱动建模和数据驱动建模的特点。结构方程模型在地学数据建模中主要面临以下三个方面的挑战,一是从主要面向验证性建模分析的方式向探索性建模分析的方式转变;二是从有完整地学模型约束的建模型方式向弱模型/无模型约束的地学数据建模方式转变;三是从无空间属性的统计变量建模向空间统计变量建模的转变。这对模型本身和数据建模的方法都提出了新的要求。针对以上三个问题,文章在回顾结构方程模型的概念和发展历程的基础上,介绍了三个结构方程模型在地学数据建模中的应用案例,一是利用湖泊沉积物地球化学数据在弱约束条件下提取地球化学金矿内生控矿因子的建模案例;二是利用结构方程模型的综合参数优化方法,通过计算后验概率与观察后验概率的匹配约束来弱化、校正证据权模型中证据独立性问题在计算金矿找矿后验概率中的影响;三是利用结构方程模型来研究墨西哥马格达莱纳流域森林保护策略,通过对不同区域的森林区块进行编号,将空间分布数据转变为传统的无空间属性的统计变量,并分析了不同环境策略对森林保护的影响。 相似文献
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在这里所研究的回归分析中,因变量是n个样品的序号——有序变量。我们用迭代法求出m个自变量的线性组合,使其各分量的大小顺序与原给的样品序号尽可能一致,文中用具体实例说明了这种方法在矿床统计预测中的应用。 相似文献
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多元线性回归方法的可靠性如何呢?上述研究者以美国的二叠系盆地为评价对象,对该方法进行验证,所取得的结果是令人满意的,和其它方法所得的结果很接近。 相似文献
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O. Brotzen 《Mathematical Geology》1975,7(3):191-214
Large sets of rock compositions can be grouped by the analysis of corresponding point distributions in multidimensional space,
where the separate dimensions represent the chemical variables. The point density around each composition is estimated in
a small, multivariate, rectangular interval. Establishment of the identity of specimens contained in the interval, and systematic
comparison of the point densities surrounding each of them, leads to the recognition of density maxima, which represent the
statistical modes of the rock types present in the set. The remaining specimens by the same operations can be assigned to
the groups formed. Experimental results are given for two sets, one comprising a wide variety of metasedimentary and metavolcanic
rocks from the ore-bearing Skellefte district and adjacent parts of V?sterbotten, Sweden, and the other representing a less
variable population of basic rocks from Norrbotten, Sweden. Linear discriminant analysis and existing geological information
indicate that the groups obtained are statistically and geologically valid. 相似文献
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The conventional paradigm for predicting future reservoir performance from existing production data involves the construction of reservoir models that match the historical data through iterative history matching. This is generally an expensive and difficult task and often results in models that do not accurately assess the uncertainty of the forecast. We propose an alternative re-formulation of the problem, in which the role of the reservoir model is reconsidered. Instead of using the model to match the historical production, and then forecasting, the model is used in combination with Monte Carlo sampling to establish a statistical relationship between the historical and forecast variables. The estimated relationship is then used in conjunction with the actual production data to produce a statistical forecast. This allows quantifying posterior uncertainty on the forecast variable without explicit inversion or history matching. The main rationale behind this is that the reservoir model is highly complex and even so, still remains a simplified representation of the actual subsurface. As statistical relationships can generally only be constructed in low dimensions, compression and dimension reduction of the reservoir models themselves would result in further oversimplification. Conversely, production data and forecast variables are time series data, which are simpler and much more applicable for dimension reduction techniques. We present a dimension reduction approach based on functional data analysis (FDA), and mixed principal component analysis (mixed PCA), followed by canonical correlation analysis (CCA) to maximize the linear correlation between the forecast and production variables. Using these transformed variables, it is then possible to apply linear Gaussian regression and estimate the statistical relationship between the forecast and historical variables. This relationship is used in combination with the actual observed historical data to estimate the posterior distribution of the forecast variable. Sampling from this posterior and reconstructing the corresponding forecast time series, allows assessing uncertainty on the forecast. This workflow will be demonstrated on a case based on a Libyan reservoir and compared with traditional history matching. 相似文献
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三联点构造地质变量的研究 总被引：1，自引：0，他引：1
地质变量的研究包括地质数据类型、地质变量类型、地质变量的选取原则和方法、地质变量变换的方法.着重对三联点构造的3个基础模型变量的选取及用数学统计方法处理变量提供思路并提出了应用前景. 相似文献
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《Chemie der Erde / Geochemistry》2021,81(4):125794
Several machine learning approaches have been developed for the identification of geochemical populations. In these approaches, the geochemical elements are usually the sole quantitative variables used as inputs for geochemical population recognition. This means that the presence of other qualitative variables, such as geological information, is overlooked in the analysis. Hierarchical clustering, as an unsupervised machine learning method, is a common approach for dimensional reduction in the analysis of geochemical data. In this study, an alternative to this technique, known as geostatistical hierarchical clustering (GHC), is applied to identify geochemical populations in 3D in the Bondar Hanza copper porphyry deposit, Iran. In this paradigm, the qualitative geological variables can also be incorporated for geochemical population identification, in addition to qualitative geochemical elements. In this study, an innovative solution is presented to tune the weighting parameters of each variable in GHC, based on the associations that the clusters (i.e., geochemical populations) should have with the geological information. The results are compared with k-means and number–size fractal/multifractal (N–S) methods. As a result, GHC showed better agreement with alterations, rock types, and mineralization zones in this deposit. Finally, some important instructions are provided for further mineral exploration. 相似文献
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Farshid Vahedifard Sona Sehat James V. Aanstoos 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(3):257-271
This study investigated the importance of rainfall and various geomorphological and geometrical factors to the vulnerability of earthen levees to slump slides. The study was performed using a database including 34 slump slides that occurred in the lower Mississippi River levee system from 2008 to 2009. The impact of rainfall within the six months prior to slide occurrence was studied for 23 slides for which an accurate occurrence date was available. Several variables were used to develop a logistic regression model to predict the probability of slump slide occurrence. The proposed model was verified for both slide and non-slide cases. The regression analysis depicts the impact of channel width, river sinuosity index, riverbank erosion, channel shape condition and distance to river. Excluding the sinuosity index, the impact of the other independent variables examined was found to be significant. Occurrence of riverbank erosion around the slide locations was the most significant predictor factor. A channel width of less than 1000?m was ranked as the second most significant variable. The proposed model can aid in locating high-risk areas on levees in order to take prompt protective measures, increase monitoring efforts and enable early response under emergency conditions. 相似文献
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近些年来,神经网络技术在矿产普查与勘探中被广泛应用,但是神经网络技术在成矿预测中存在明显的不足,它只是根据目标模式识别输入模式,而没有直接考虑变量和地质单元之间的关系,而且预测结果的可靠性很难评价。而特征分析方法是通过研究区内已知地质单元的研究,查明地质变量之间的内在联系并确定它们的找矿意义。因此,以华北地块北缘的铅锌矿成矿预测为例,把神经网络技术和特征分析结合起来进行成矿预测,可以弥补神经网络技术的不足,还可以提高预测成果的可靠性。这对找矿工作有很好的实际意义。 相似文献