Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area,Southern Spain

Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered.     

Using a Discrete Fracture Network and Spatial Point Processes to Populate Veins and Model Grade in a Coarse Gold Deposit

Vein-hosted gold deposits are characterized by mineralization, which is spatially restricted to narrow vein structures. Drillholes intersecting a mineralized vein can lead to unreliable and biased assay values compared to selective mining unit scale block grades. In this work, a discrete fracture network is simulated and adapted to model gold mineralization within the veins. Veins are assumed planar and the required inputs are distributions of vein orientation, vein length, and vein intensity (i.e., density). These inputs are collected from drillhole data, geological mapping, and expert knowledge of the deposit. A spatial point process is then applied to model gold grade as discrete events or “nuggets,” which are spatially restricted to the simulated quartz veins for the case of incomplete mineralization of the veins; when the vein is completely mineralized, a vein thickness distribution is required. The methodology is applied to an epithermal gold deposit in northwestern British Columbia, Canada and shows improvement in restricting the influence of the high-grade gold samples without resorting to ad-hoc manipulation of input assays through capping or cutting. The final output of this methodology is a block model of gold grade, which better honors the spatial structure of the veins in the deposit and is suitable for use in mine planning or resource estimation.     

陆地表层系统分类研究进展——从土地类型到地球关键带类型

陆地表层系统是多圈层相互作用下形成的综合体,具有时空异质性和层次性。对陆地表层系统进行多层级类型划分是识别其空间分异格局和演替规律的重要方法,可为多尺度地球科学系统研究和自然资源统一管理提供科学依据。陆地表层系统分类其实质是在特定尺度下对其组成要素间的空间和关系的划分,故土地类型、地表基质、关键带类型等都可归纳为陆地表层系统分类方案。论文在对国内外典型陆地表层系统分类方案进行梳理的基础上,将陆地表层系统分类总结为探索—发展—完善3个发展阶段。继而,对比了各分类体系内涵,提出各分类体系虽分属不同学派,但均是以陆地表层系统为研究对象,是研究陆地表层系统结构、功能和空间分异的方法论;分析了各分类体系结构的异同,总结了各分类体系在层级、比例尺和要素方面的共同特征。在此基础上,提出构建一套从地块到国家的陆地表层系统分类设想,并对等级体系、分类体系及分类方法集成3个方面进行了展望。     

Non-stationary Geostatistical Modeling: A Case Study Comparing LVA Estimation Frameworks

Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.

     

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added     

云南勐满金矿硅质岩特征及其与金矿化关系

云南勐满金矿位于临沧——勐海花岗岩体西接触带之小南婆——西定铁金多金属成矿区,矿区内硅质岩发育。显微镜下观察,硅质岩无明显的生物迹象。而化学成分分析显示,W(Al)/W(Al+Fe+Mn)值远大于0.35,不具有热水沉积作用典型的高铁锰特征。综合各现象可初步判定硅质岩属交代沉淀成因。硅质岩的形成与金矿化之间存在密切的联系,因而可将硅质岩作为金矿勘探开发的一条重要找矿标志。     

Multi-scale Numerical Simulation and 3D Modeling for Deep Mineral Exploration in the Jiaojia Gold District,China

In this study, deposit- and district-scale three-dimensional (3D) fault-and-intrusion structure models were constructed, based on which a numerical simulation was implemented in the Jiaojia gold district, China. The numerical simulation of the models shows the basic metallogenic path and trap of the gold deposits using mineral system theory. The objective of this study was to delineate the uncertainty of the geometry or buffer zones of the ore-forming and ore-controlling fault-and-intrusion domains in 3D environment representing the exploration criteria extraction and the gold potential targeting in the study area. The fast Lagrangian analysis of continua in three dimensions was used as the platform to define the stress deformation fracture ore storage and the hydrothermal seepage channel zone based on the gold deposit features and metallogenic model in the study area. The validity of the numerical simulation was verified by comparing it with robust 3D geological models of the large Xincheng gold deposit. The potential targeting zones are analyzed for uncertainty and then evaluated by Boolean operation in a 3D geological model using the computer-aided design platform. The research results are summarized as follows. (1) In the pre-mineralization period, the Jiaodong fault’s left lateral movement created the Jiaojia network faults and formed a fracture zone with NW- to NNW-trending dips of 20° to 40°. (2) During the mineralization period, hydrothermal flow was associated with the intrusion geometry and features. However, it was constrained by the Jiaojia fault, which blocked the vadose flow into the upper wall rock and made the hydrothermal route close to the fault in the footwall fracture zones. (3) Three gold potential targets were identified by the numerical simulation results in the study area: the NW-trending Sizhuang gold deposit, the NW-trending zone of Jiaojia gold deposit, and the NE-trending zone of the Xincheng gold deposit. (4) The numerical simulation results show the fault-and-intrusion metallogenic domain and the hydrothermal alteration zones, which reflect the main ore-controlling and ore-forming factors of mineralization. The information obtained through the numerical simulation discussed here can be used to define exploration criteria in the study area.

     

Modeling-Based Multiscale Deep Prospectivity Mapping: A Case Study of the Haoyaoerhudong Gold Deposit,Inner Mongolia,China

The mineral system modeling approach for prospectivity mapping is an efficient and economic method to assess undiscovered mineral potential quantitatively. It is a procedure of modeling, acquiring, and coupling the proxies of footprints of mineral systems at multiple scales (e.g., regional, district, and deposit scales). In this approach, the critical issue from multiple scales is that the data collected are asymmetrical from the superficial to the deep or from mine to its brown fields, so that it is hard to employ and integrate them. To complete this study, firstly, multi-tactic 3D geological modeling methods, including the explicit, the implicit, and inversion, were used to build geological models in the condition of asymmetrical datasets at the deposit and district scales. Secondly, indicators acquired in drill-intensive fields among multisource datasets composed of geology, geochemistry, geophysics and alteration data were transferred to studies in deep and brown fields. Finally, deep (~?1,100 m) and circumjacent potentials of mine were targeted in the Haoyaoerhudong gold deposit situated in the Urad Middle Banner area, Inner Mongolia, which is one of the largest black-rock-series-type gold mines in China. This proposed procedure is more visual, clear, intuitive, and transferable to drive mineral system approach to exploration discovery than previous GIS-based studies.

     

Selection of Optimal Thresholds for Estimation and Simulation Based on Indicator Values of Highly Skewed Distributions of Ore Data

This study strives to outline a geostatistics model for estimation and simulation of the Qolqoleh gold ore deposit located in Saqqez, NW of Iran. Considering that this gold deposit contains high-grade values, accurate evaluation of such values is of high importance, and therefore different methods based on indicator values, such as full indicator kriging (FIK) and sequential indicator simulation (SIS), have been employed to improve the accuracy of estimation and simulation of high-grade values. FIK and SIS cover the full range of grades based on several thresholds on the indicator data. The cumulative distribution function (CDF) is typically used for selection of threshold values. Given the highly skewed distribution of gold grade and its intense fluctuations, the number of thresholds is increased using CDF, which in turn results in a whole lot of calculations. To reduce the volume of calculations, the number–size (N–S) fractal model has been used to select thresholds. From such a model, all optimal thresholds are chosen with respect to geology and the unnecessary thresholds are excluded from selection. Thus, a study of the selection of optimal thresholds for estimation and simulation of a gold ore resource by means of FIK and SIS, respectively, based on thresholds selected using the N–S fractal model is presented. Finally, it is proved that results of these geostatistical methods based on thresholds selection from the N–S model appear to be better-positioned to explain ore grade variability compared to thresholds selected from the CDF and threshold selection from the N–S model is more effective for reducing the volume of required calculations.     

Predictive Mapping of Prospectivity in the Gurupi Orogenic Gold Belt,North–Northeast Brazil: An Example of District-Scale Mineral System Approach to Exploration Targeting

The Gurupi Belt hosts a Paleoproterozoic gold province located in north–northeastern Brazil, at the borders of Pará and Maranhão states. It is considered to be an extension of the prolific West African Craton’s Birimian gold province into South America. Additionally, the belt has been the object of recent mineral exploration programs with significant resource discoveries. This study presents the results of predictive mapping using up-to-date mineral system concepts and recently finished regional-scale geological mapping, stream sediment and airborne geophysical surveys conducted by the Geological Survey of Brazil. We relate gold mineralization to an initially enriched crust, metamorphism, deep fluid pathways, structurally controlled damage zones and hydrothermal alteration. Prospective targets were generated using only regional public datasets and knowledge-driven targeting technique. This work did not incorporate any known gold deposits, yet it predicted the largest known deposits and their satellite targets. Besides, high prospective targets mapped almost 40% of known primary gold occurrences within 7% of the project area. This work allowed considerable search area reduction and identification of new target areas, thus collaborating on reducing costs, time and risk of mineral exploration. Results indicate that we achieved an efficient understanding of the geological processes related to the Gurupi Belt mineral system.     

基于数值模拟的黄河下游不同情景溃堤洪水特性

通过研究黄河下游地区的溃堤洪水特性,为防洪减灾工作提供科学依据。本文以黄河下游地区的DEM,1958、1982、1996年典型历史洪水和2000年土地利用数据为模型初始输入,分4个模拟情景,依据黄河溃堤水流的特性,利用二维非恒定流模型对黄河下游不同位置决口情景下的洪水演进特性进行模拟,结果表明:洪水在演进过程中都会侵夺沿途河流行洪,进入下游后洪水会顺河归槽;在同一河段,同一年型洪水北岸不同位置溃口情景所淹没的面积大于南岸对应位置的淹没面积;黄河两岸洪泛区地理位置的差异,使两岸的洪灾程度具有区域差异;水深且流速大的洪水淹没主流区面积相对较小,水浅且流速小的非主流区面积相对较大。     

基于DEM的复杂地形流域特征提取

利用流域数字高程模型(DEM)构建数字水系模型并提取流域水系特征是分布式水文过程模拟的重要前提。提出了面向分布式水文过程模拟和流域特征提取的数字水系模型,并针对现有方法对复杂地形DEM中含有的平地、洼地及其嵌套情形的处理不足,提出了栅格水流分类、填洼分类与归并及有效填平处理、河谷平地的出流代价法构建栅格流向和流序等新的处理方法,并在开发的软件系统得到实现。使用该方法创建的黄土岭流域数字水系模型和提取的水系等流域特征结果表明:本文方法可有效应对复杂地形流域的处理,提取的流域水系特征与实际自然水系比较吻合,能够有效地消除现有方法在地形平坦区域容易产生的平行河道、奇异河道、河道变形等不足。     

An indigenous soil classification system for Bellona Island – a raised atoll in Solomon Islands

One of the challenges of evaluating existing traditional farming systems is to combine local knowledge and modern scientific methods and terminology. This requires an evaluation of indigenous soil classification in modern terms. This paper focuses on an indigenous soil classification system developed by local farmers on the island of Bellona, Solomon Islands. The definitions of the different soil types are described and the principles of the classification system and the applicability of ethnopedology in soil surveys are discussed. Based on interviews with about 20 per cent of the farmers on the island as well as standard soil chemical and physical determinations on main soil types, an evaluation of the soil types for cultivation of various crops is carried out. The soils on Bellona are developed on oolitic or clayey phosphate-rich deposits forming the basis for the agriculture production on the island. The Bellonese soil classification system is mainly based on the physical properties of the humus-containing top layer. Subsoil layers are only used for classification if they are very close to the surface and may be mixed with the topsoil. Results show a general agreement among farmers, who perceive the same four out of seven soil types as highly useful for cultivation and rank these soil types similarly according to their suitability for different crops such as yam, watermelon, cassava and sweet potato. It is concluded that the indigenous soil classification is in line with the soil production potential and useful for land evaluation on Bellona.     

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.     

基于DSR的城乡结合部土地价格影响因素体系的构建

受城市与农村的双重影响,城乡结合部是城市发展与变化的聚焦地带。目前对城乡结合部土地价格的评估方法探讨较多,而对地价影响因素研究较少。合理确定土地价格影响因素有利于客观、快速地评估土地价格。该文以可持续发展为总体目标,采用D—S—R模型和因果分析法,参考有关文献资料,构建城乡结合部土地价格影响因素体系。分析城乡结合部土地价格特点,提出土地价格影响因素的D—S—R概念模型;尝试构建土地价格影响因素体系,并将社会因素和环境因素纳入该体系中;将其中的因子分为两大类,分别介绍其分值计算方法。该体系的建立可以为动态土地估价和土地利用决策提供依据。     

Categorization of Mineral Resources Based on Different Geostatistical Simulation Algorithms: A Case Study from an Iron Ore Deposit

Mineral resource classification plays an important role in the downstream activities of a mining project. Spatial modeling of the grade variability in a deposit directly impacts the evaluation of recovery functions, such as the tonnage, metal quantity and mean grade above cutoffs. The use of geostatistical simulations for this purpose is becoming popular among practitioners because they produce statistical parameters of the sample dataset in cases of global distribution (e.g., histograms) and local distribution (e.g., variograms). Conditional simulations can also be assessed to quantify the uncertainty within the blocks. In this sense, mineral resource classification based on obtained realizations leads to the likely computation of reliable recovery functions, showing the worst and best scenarios. However, applying the proper geostatistical (co)-simulation algorithms is critical in the case of modeling variables with strong cross-correlation structures. In this context, enhanced approaches such as projection pursuit multivariate transforms (PPMTs) are highly desirable. In this paper, the mineral resources in an iron ore deposit are computed and categorized employing the PPMT method, and then, the outputs are compared with conventional (co)-simulation methods for the reproduction of statistical parameters and for the calculation of tonnage at different levels of cutoff grades. The results show that the PPMT outperforms conventional (co)-simulation approaches not only in terms of local and global cross-correlation reproductions between two underlying grades (Fe and Al₂O₃) in this iron deposit but also in terms of mineral resource categories according to the Joint Ore Reserves Committee standard.

     

On a proposed plutonic porphyry gold deposit model

A plutonic porphyry gold deposit model is proposed that is imilar to the plutonic porphyry copper deposit model. However, unlike the plutonic porphyry copper deposit model, the proposed model is deficient in copper and contains less than 1 percent total sulfides. In the proposed model, gold is accompanied by scheelite, molybdenite, arsenopyrite, a variety of bismuth sulfides, tellurides, and native bismuth. The host rock varies from granite to granodiorite stock. Most of the ore is in the pluton. Deposits cited as examples of the proposed model are the Mokrsko deposit in Czechoslovakia, the Fort Knox deposit in the United States, and the Dublin Gulch deposit in Canada. In each of these deposits, pervasive potassic or phyllic alteration zones accompany the gold ore, which is disseminated in quartz-rich stockworks, veinlet swarms, and veins. Tonnages of gold-bearing material are large, but grades are low in the cited deposits. The proposed model is distinct from other gold deposit models because of the low Cu to Au ratio and the association of Au, Bi, W, and Mo.     

Comments on thaw-freeze algorithms for multilayered soil, using the Stefan equation

The Stefan equation provides a useful and widely used method for predicting the depth of thawing and freezing in a soil where little site-specific information is available. The original Stefan equation was derived for only a homogeneous medium, and some algorithms have been developed for its use in a multilayered system. However, although the Stefan equation was derived more than 100 years ago, there is not a unified understanding for its use in a multilayered system. This paper examines the use of the Stefan equation in multilayered soil, based on comparing three algorithms(JL-algorithm, NM-algorithm, and XG-algorithm). We conclude that the JL and NM algorithms are incorrect, as they arose from flawed mathematical derivations. Both of these algorithms failed to recognize that the thawing depth in a multilayered soil is a piecewise function and not a continuous function of time. This work asserts that the XG-algorithm is a correct and rigorous method to determine the freezing–thawing fronts in multilayered soil.