数学地质在激发极化法中的应用

激发极化法测得的四组参数ρ、ｈ、Ｊ和Ｄ，是判断煤矿采空区积水情况的重要依据。但在传统的资料处理过程中，作图量大，且异常特征不明显时又难以作了准确的判断。而数学地的聚类。而数字地质中聚类、判别分析方法，可以很好地解决这一难题。本文以实例介绍了这种方法的应用效果。     

U.K.-U.S. WORKSHOP ON INTERFACING MICRO- AND MACRO-MODELS OF SPATIAL BEHAVIOR

Five semiarid Montana ghost towns abandoned for more than 45 years were studied to understand better the nature of soil and vegetation recovery following severe human impacts. Discriminant analysis was used to interpret and classify variation among land-use intensity groups. Recovery at the five towns was strongly linked to the degree of the initial soil disturbance, vegetation type, and precipitation. Recovery of the vegetation to ambient conditions was far from complete in all but one town.     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

利用费希尔最优分割法划分临夏盆地早更新统地层孢粉带的初步研究

将费希尔（Fisher）最优分割法引入到临夏盆地早更新统东山组地层孢粉带的划分中，结合地层特征，共划分出了3个孢粉带。这3个孢粉带分别为：带Ⅰ（深度78．5～45．5m）柏科-禾本科组合带、带Ⅱ（深度45．5～12．5m）云杉属-榆属-禾本科组合带、带Ⅲ（深度12．5～0．5m）云杉属一禾本科组合带。孢粉带分带揭示，临夏盆地早更新世气候变化经历了干-湿-干3个阶段，这一结果同以往获得的粒度、碳酸钙和Cl-等无机指标高分辨率分析结果相一致。     

运用机器学习和锆石微量元素构建花岗岩成矿潜力判别图解：以东昆仑祁漫塔格为例

由于锆石在中酸性岩中广泛存在且成分稳定、不易受到后期热液活动的扰动,因此锆石成分可以有效记录成矿岩浆信息。其中,锆石的Ce4+/Ce3+、Ce/Ce*、Eu/Eu*和Ce/Nd值可以反映岩浆氧逸度和含水量等成矿信息,已被广泛用于花岗岩类成矿潜力评价。然而,随着研究的深入发现,这些地球化学指标并不完全具有普适性。此外,以往研究均是根据对成矿岩体的“已知认识”提出成矿潜力判别方法,但考虑到成矿过程的复杂性,许多反映岩浆成矿能力的地球化学信息可能均尚未被揭露。为此,笔者以东昆仑祁漫塔格成矿带为例,借助当前广泛应用的机器学习算法之一——支持向量机,对来自该成矿带斑岩−矽卡岩Cu−Fe−Pb−Zn多金属矿床成矿岩体和全球非成矿岩体的锆石数据开展机器学习训练,目的在于挖掘能够反映岩浆成矿能力的锆石微量元素特征,从而构建花岗岩成矿潜力判别图解。模型训练结果显示,在21个常见的锆石微量元素特征中,Gd、Dy、Yb、Y、Tm等5种元素特征对识别岩浆成矿能力最为重要。在此基础上,笔者新建立了10个二元判别图解,它们在识别成矿岩体和非成矿岩体时的准确率均接近1。研究表明,利用机器学习方法和地质大数据,可以挖掘传统研究方法难以发现的新的地球化学指标和图解,这对深入认识矿床成因、指导找矿勘查具有重要意义。     

Applicability of P/S amplitude ratios for the discrimination of low magnitude seismic events

Applicability of regional P/S amplitude ratios for the discrimination of low-magnitude seismic events was tested and proved using earthquakes and explosions in Central Asia. Results obtained show that regional P/S amplitude ratios which may discriminate medium or large magnitude events well, are also applicable to low magnitude events. Their performances for low magnitude events are almost as good as that for medium or large events. Statistical comparisons based on 25 P/S discriminate from the four seismic stations WMQ, BLK, MUL and MAK showed that the average misclassification rate for low-magnitude seismic events averagely was only 2 percent higher than that for medium and large magnitude seismic events.     

逐步判别分析法在筛选水质评价因子中的应用

应用逐步判别分析法对水质进行评价因子筛选,通过对水质实际监测因子的假设检验分析,引入判别能力好的因子,建立判别方程。对实例评价结果表明：通过筛选引入判别方程变量的后验概率均达到90%以上,对判别分类有显著影响,从而提高了所建立的判别函数的稳定性和评价结果的可靠性。进行因子筛选的评价结果显示石头口门水库2001-2004年为Ⅱ类水,2005年为Ⅲ类水,污染呈现出逐年加重之势,与实际情况相符;而未进行因子筛选的评价结果显示5年水质没有变化,均为Ⅱ类水,且2005年后验概率仅达到52％,结果判误率高。     

镁铝榴石的化学成分分类及其在金刚石找矿中的指示意义

镁铝榴石是找寻金刚石的一种重要的指示矿物。本文应用了聚类分析方法，以镁铝榴石中的钙组分、铬组分、镁组分和镁组分（铁铝榴石）4种成分参数作变量，将56个样品分为6类，讨论了每一类样品的化学成分特点，确定了它们的标型意义，并应用多元判别分析得出了划分这6类和对未知样品进行判别归类的一组判别函数。