Integration of Structural,Gravity, and Magnetic Data Using the Weights of Evidence Method as a Tool for Kimberlite Exploration in the Buffalo Head Hills,Northern Central Alberta,Canada

In the Buffalo Head Hills area a weights of evidence statistical approach was used to determine the spatial relationship of NNE-, NE-, NW-, and ENE-trending lineaments to known kimberlite locations. This method outlined different degrees of spatial correlation between and lineaments, with higher correlations defined for the NNE, NE, and ENE lineament data sets. A weights of evidence model then was constructed using the structural lineament maps, the Buffalo High and Buffalo Utikuma terrane boundary, Bouguer gravity data, and magnetic characteristics of the Buffalo High and Buffalo Utikuma terranes. The model reveals maximum favorability for kimberlite exploration along the Buffalo High and Utikuma terrane boundary in correspondence with NNE-trending lineaments and their intersections with NE and ENE lineaments. The relationship of the kimberlite occurrences along the Buffalo High-Buffalo Utikuma terrane boundary and structural lineaments seems to favor an hypothesis of kimberlite emplacement through a major zone of weakness in the basement, here characterized by the boundary between the Buffalo High and Buffalo Utikumaterranes.     

Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States

The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km² as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.     

Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines

Binary predictor patterns of geological features are integrated based on a probabilistic approach known as weights of evidence modeling to predict gold potential. In weights of evidence modeling, the log _e of the posterior odds of a mineral occurrence in a unit cell is obtained by adding a weight, W ⁺ or W ^– for presence of absence of a binary predictor pattern, to the log _e of the prior probability. The weights are calculated as log _e ratios of conditional probabilities. The contrast, C = W ⁺ – W ^–, provides a measure of the spatial association between the occurrences and the binary predictor patterns. Addition of weights of the input binary predictor patterns results in an integrated map of posterior probabilities representing gold potential. Combining the input binary predictor patterns assumes that they are conditionally independent from one another with respect to occurrences.     

面向对象的山地区域多源遥感影像分割尺度选择及评价

随着遥感技术的飞速发展,通过各种传感器获取的影像种类越来越多.传统的面向像元的影像分析技术已难以适应高分辨率影像,面向对象的影像分析技术应运而生.面向对象的影像分析技术所面临的首要问题是影像分割,如何选择合适的影像分割尺度已成为面向对象影像分析技术的关键.为了提高遥感影像分割尺度选择的准确性,通过对影像分析建立了分割质量函数,针对不同分辨率的多源遥感影像进行分割实验,获取了最优分割尺度,最后提出了一种顾及面积和光谱的分割匹配指数对最优分割尺度进行评价,并研究了分割尺度与亮度均值标准差、影像空间分辨率之间的对应关系.研究结果表明,构建的分割质量函数获取的最优分割尺度避免了人工分割带来的主观性,建立的同时顾及面积和光谱因子的分割匹配指数验证了最优分割尺度选择的正确性.     

Mineral-Potential Mapping for MVT Deposits with Limited Data Sets Using Landsat Data and Geological Evidence in the Borden Basin, Northern Baffin Island, Nunavut, Canada

Posterior probabilities of occurrence for Zn-Pb Mississippi Valley Type (MVT) mineralization were calculated based on evidence maps derived from regional geology, Landsat-TM, RADARSAT-1, a digital elevation model and aeromagnetic data sets in the Borden Basin of northern Baffin Island, Canada. The vector representation of geological contacts and fault traces were refined according to their characteristics identified in Landsat-TM, RADARSAT-1, DEM, slope, aspect, and shaded relief data layers. Within the study area, there is an association between the occurrence of MVT mineralization and proximity to the contact of platformal carbonates and shale units of the adjacent geological formation. A spatial association also tends to exist between mineralization and proximity to E-W and NW-SE trending faults. The relationships of known MVT occurrences with the geological features were investigated by spatial statistical techniques to generate evidence maps. Supervised classification and filtering were applied to Landsat-TM data to divide the Society Cliffs Formation into major stratigraphic subunits. Because iron oxides have been observed at some of the MVT occurrences within the Borden Basin, Landsat-TM data band ratio (3/1) was calculated to highlight the potential presence of iron-oxides as another evidence map. Processed Landsat-TM data and other derived geological evidence maps provided useful indicators for identifying areas of potential MVT mineralization. Weights of evidence and logistic regression were used independently to integrate and generate posterior probability maps showing areas of potential mineralization based on all derived evidence maps. Results indicate that in spite of the lack of important data sets such as stream or lake sediment geochemistry, Landsat-TM data and regional geological data can be useful for MVT mineral-potential mapping.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

Variability and spatial modeling of fine-scale precipitation data for the Sonoran Desert of south-west Arizona

We present a unique new set of high spatial resolution precipitation data from a storage gauge network, for the sparsely observed northern Sonoran desert in south-west Arizona. We examine the nature and causes of the highly complex seasonal and spatial variability in the data, using fine-scale maps developed via spatial modeling and interpolation. These high-resolution maps had explained variances approaching 1·00, and precipitation errors of about 1% in winter and about 10% in summer. Seasonal precipitation ranges from near zero to almost 15 in across the area, and shows high interannual variability. Localized convectional processes lead to summer anomalies that are more spatially complex than in winter when broad-scale synoptic and frontal processes cause precipitation. In general, summer and winter precipitation variability are tied to meridional-zonal shifts and east–west movement of the respective anticyclone or trough pattern over the region. Statistical links between major weather stations in the region and precipitation across the area are spatially inconsistent, especially in the west.     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.     

耦合高维排序权和对偶线性规划的综合指数算法

区域可持续与高质量发展需定期监测并科学评估。综合指数评价是应用最为广泛、信息传输效率最高的评估方法。该方法将表征区域发展质量不同侧面的系列指标加权聚合为单个指数,其核心是采用或客观或主观的方式确权。客观确权基于指标的数值统计特性实现,故权重值随指标值而改变,实非“客观”;主观确权能反映决策者智慧,但指标过多时难以实现。学界新提出的基于耦合排序权和熵权法的综合指标法允许决策者确权时仅给出权重排序,但仅支持低维情况（限制为3个指标）。本文在其基础上,借助对偶线性规划推导,使其适用性不再受限于指标数（即实现高维排序权）。同时,对指标的聚合方式进行修改,扩大了算法的适用范围;对熵权法的使用进行修正,增强了算法结果的可解译性。基于推导结果,本文进一步发展出单排序、复排序、全排序3种不同模式下的综合指数计算方法,以满足决策者主观性强、弱、无等不同情况。最后,将算法用于全球可持续发展格局的时空评价分析。本文新发展的算法同时适用于高低不同维度的指数聚合、可兼顾决策者不同层次的主观参与度,具有较强的普适性。     

基于主成分与广义回归神经网络耦合的寒区水库裂缝开合度预测模型

水库裂缝开合情况对于水库的安全运行极为重要.将主成分分析法与广义回归神经网络结合在一起,进行水库裂缝开合度的预测.结果表明:应用主成分分析与广义回归神经网络相耦合的模型可以很好的反映环境因子(水压力因子、温度因子、时效因子)与水库裂缝开合度之间的非线性函数映射关系.同时利用Matlab软件对新疆某寒区水库裂缝的开合度进...     

矿粮复合区土地生态安全评价——以焦作市为例

应用PSR模型构建焦作市矿粮复合区土地生态安全评价指标体系,通过AHP法与熵权法相结合确定24个评价指标的权重,对其2006-2010年的土地生态安全状况进行动态评价与分析,旨在揭示该区域近年来的土地生态安全状况,提出实现其土地可持续利用的建议。结果表明：其土地生态安全综合指数总体呈现先短期轻微下降后又逐年回升的趋势;5 a内其安全水平先后经历了较不安全、临界安全、较安全3个等级;状态与响应指标与土地生态安全状况的相关性密切,对环境胁迫的恢复力起重要作用,且在维护和改善土地生态安全状况上的作用不断增强;植被因素对土地生态安全的正作用最明显,生态用地比例等4项指标在土地生态安全评价中的地位相对较重要;主控生态安全的因子越多,生态状况越好;未来3 a,其生态状况有继续提升的趋势。实现土地可持续利用须从不同方面通过多种手段进行。该研究为实现区域农业生产和工矿建设等经济活动与土地资源和生态环境保护之间的协调发展提供决策依据。     

黄土高原高含沙水流形成的自然地理因素

黄土高原半干旱的气候条件、稀疏的植被和疏松易蚀的黄土物质,是该地区高含沙水流形成的自然地理因素。这一地区高含沙水流的发育程度,特别是最大断面含沙量的大小表现出明显的地域差异,这种差异与悬移质泥沙的粒度组成有密切的关系。存在着某种特定的悬移质泥沙粒度组成,使得悬移质含沙量达到最大值。这种特定的泥沙粒度组成与流域中黄土、基岩及风成沙的分布有关,而它的出现,则是特殊的水力、风力交换作用的结果。     

紫色土经济林栽培技术

为了合理开发利用紫色土 ,保护生态环境 ,为紫色土经济林栽培提供实践指导 ,对紫色土经济林栽培技术进行了研究 测定了全垦、带垦、水平梯土、水平梯土撩壕、鱼鳞坑等 5种整地方法的土壤以及对照地 (不整地 )的土壤侵蚀量 ,观测了各种整地方法对经济林木生长发育的影响 ,同时 ,对栽植在紫色土和板页岩红壤上的经济林木的生长发育情况进行了对比 结果表明 :不同整地方法土壤侵蚀量按从大到小的顺序排列为全垦 (81 4t·hm-2a-1,以下单位均为t·h-2 a-1) >带垦 (63 6) >水平梯土 (3 8 7) >水平梯土撩壕 (3 1 8) >鱼鳞坑 (2 4 3 ) >对照(2 1 1) ;不同整地方法对经济林木生长发育的影响按由好到差的顺序排列为水平梯土撩壕、水平梯土、全垦、带垦、鱼鳞坑、对照 ;在一般抚育管理条件下 ,在紫色土坡地生长发育较好的树种或品种有板栗、枣树、杨梅、枇杷、萘李、甜柿、水蜜桃 ,生长发育一般的树种有茶树、无核蜜柑、柚子 ,生长发育较差的树种有脐橙、柑、葡萄 因此 ,在坡度不大 (<2 5°)的紫色土坡地营造经济林时 ,整地方法采用水平梯土撩壕为宜 ,栽培树种可选择板栗、枣树、杨梅、枇杷、萘李、甜柿、水蜜桃 ,还可选择无核蜜柑、茶树、柚子等