Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km² with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.     

A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.     

Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.     

Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites,Lundazi District,Zambia

A case application of data-driven estimation of evidential belief functions (EBFs) is demonstrated to prospectivity mapping in Lundazi district (eastern Zambia). Spatial data used to represent recognition criteria of prospectivity for aquamarine-bearing pegmatites include mapped granites, mapped faults/fractures, mapped shear zones, and radioelement concentration ratios derived from gridded airborne radiometric data. Data-driven estimates EBFs take into account not only (a) spatial association between an evidential map layer and target deposits but also (b) spatial relationships between classes of evidences in an evidential map layer. Data-driven estimates of EBFs can indicate which spatial data provide positive or negative evidence of prospectivity. Data-driven estimates of EBFs of only spatial data providing positive evidence of prospectivity were integrated according to Dempster’s rule of combination. Map of integrated degrees of belief was used to delineate zones of relative degress of prospectivity for aquamarine-bearing pegmatites. The predictive map has at least 85% prediction rate and at least 79% success rate of delineating training and validation deposits, respectively. The results illustrate usefulness of data-driven estimation of EBFs in GIS-based predictive mapping of mineral prospectivity. The results also show usefulness of EBFs in managing uncertainties associated with evidential maps.     

Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration

Index overlay and Boolean logic are two techniques customarily applied for knowledge-driven modeling of prospectivity for mineral deposits, whereby weights of values in evidential maps and weights of every evidence map are assigned based on expert opinion. In the Boolean logic technique for mineral prospectivity modeling (MPM), threshold evidential values for creating binary maps are defined based on expert opinion as well. This practice of assigning weights based on expert opinion involves trial-and-error and introduces bias in evaluating relative importance of both evidential values and individual evidential maps. In this paper, we propose a data-driven index overlay MPM technique whereby weights of individual evidential maps are derived from data. We also propose a data-driven Boolean logic MPM technique, whereby thresholds for creating binary maps are defined based on data. For assigning weights and defining thresholds in these proposed data-driven MPM techniques, we applied a prediction-area plot from which we can estimate the predictive ability of each evidential map with respect to known mineral occurrences, and we use that predictive ability estimate to assign weights to evidential map and to select thresholds for generating binary predictor maps. To demonstrate these procedures, we applied them to an area in the Kerman province in southeast Iran as a MPM case study for porphyry-Cu deposits.     

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.     

Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping

This paper proposes a new approach of weights of evidence method based on fuzzy sets and fuzzy probabilities for mineral potential mapping. It can be considered as a generalization of the ordinary weights of evidence method, which is based on binary or ternary patterns of evidence and has been used in conjunction with geographic information systems for mineral potential mapping during the past few years. In the newly proposed method, instead of separating evidence into binary or ternary form, fuzzy sets containing more subjective genetic elements are created; fuzzy probabilities are defined to construct a model for calculating the posterior probability of a unit area containing mineral deposits on the basis of the fuzzy evidence for the unit area. The method can be treated as a hybrid method, which allows objective or subjective definition of a fuzzy membership function of evidence augmented by objective definition of fuzzy or conditional probabilities. Posterior probabilities calculated by this method would depend on existing data in a totally data-driven approach method, but depend partly on expert's knowledge when the hybrid method is used. A case study for demonstration purposes consists of application of the method to gold deposits in Meguma Terrane, Nova Scotia, Canada.     

Developments in Quantitative Assessment and Modeling of Mineral Resource Potential: An Overview

Natural Resources Research - The special issue entitled “Developments in Quantitative Assessment and Modeling of Mineral Resource Potential” is composed of 17 papers that cover a...     

土壤水盐运移模型研究进展及展望

回顾了国内外土壤水盐运移模型的发展历程,根据土壤水盐运移模型构成的原理和发展趋势,将土壤水盐运移模型分为水盐平衡模型、物理模型和系统模型。借助国内外文献对已有模型的原理和应用进行了介绍,并对存在的问题进行了分析;展望了新形势下土壤水盐运移模型的未来发展趋势和研究重点,旨在为今后土壤水盐运移模型研究提供新的思路。     

From 2D to 3D Modeling of Mineral Prospectivity Using Multi-source Geoscience Datasets,Wulong Gold District,China

Natural Resources Research - The Wulong gold district (WGD) is located at the northeastern margin of the North China Carton (NCC) on the Liaodong Peninsula, China. The results of early mineral...     

蒙古国矿产资源分布与中蒙两国合作开发前景论证

习近平副主席于2008年6月访问蒙古国,并签署了《中蒙经贸合作中期发展纲要》,标志着两国经贸合作特别是矿产资源合作开发进入了新阶段。蒙古国是中国实施睦邻、富邻、安邻政策的主要对象,矿产资源合作开发又是两国经贸合作的重要领域,为此,有必要对其矿产资源分布情况、开发态势及两国合作开发利弊因素进行分析研究,有针对性地提出加大合作开发力度的对策创意。作者认定,中蒙两国合作开发矿产资源前景光明,必将为双方经贸合作的持久发展注入强劲动力。     

Application of Mineral Exploration Models and GIS to Generate Mineral Potential Maps as Input for Optimum Land-Use Planning in the Philippines

The inherent problems of classifying or inventorying potential mineral resources (as opposed to known mineral resources) pose specific challenges. In this paper, the application of a conceptual mineral exploration model and GIS to generate mineral potential maps as input to land-use policy decision-making is illustrated. We implement the criteria provided by a conceptual exploration model for nickeliferous-laterites by using a GIS to classify the nickeliferous-laterite potential of an area in the northeastern part of the Philippines. The spatial data inputs to the GIS are geological map data, topographic map data, and stream sediment point data. Processing of these data yields derivative maps, which are used as indicators of nickeliferous-laterite potential. The indicator maps then are integrated to furnish a nickeliferous-laterite potential map. This map is compared with present land-use classification and policy in the area. The results indicate high potential for nickeliferous-laterite occurrence in the area, but the zones of potential are in places where mineral resources development is prohibited. The prohibition was imposed before the nickeliferous-laterite potential was assessed by this study. Mineral potential classification therefore is a critical input to land-use policy-making so that prospective land is not alienated from future mineral resource development.     

Mineral and Lithology Mapping of Drill Core Pulps Using Visible and Infrared Spectrometry

A novel approach for using field spectrometry for determining both the mineralogy and the lithology of drill core pulps (powders) is developed and evaluated. The methodology is developed using material from a single drillhole through a mineralized sequence of rocks from central New South Wales. Mineral library spectra are used in linear unmixing routines to determine the mineral abundances in drill core pulps that represent between 1 m and 3 m of core. Comparison with X-Ray Diffraction (XRD) analyses shows that for most major constituents, spectrometry provides an estimate of quantitative mineralogy that is as reliable as that provided by XRD. Confusion between the absorption features of calcite and those of chlorite causes the calcite contents determined by spectrometry to be unreliable. Convex geometry is used to recognize the spectra of those samples that are extreme and are representative of unique lithologies. Linear unmixing is used to determine the abundance of these lithologies in each drillhole sample and these abundances are used to interpret the geology of the drillhole. The interpreted geology agrees well with conventional drillhole logs of the visible geology and photographs of the split core. The methods developed provide a quick and cost-effective way of determining the lithology and alteration mineralogy of drill core pulps.     

Determination of Predictive Variables in Mineral Prospectivity Mapping Using Supervised and Unsupervised Methods

Natural Resources Research - Machine learning methods have recently been used widely for mineral prospectivity mapping. However, few studies have focused on the determination of variables for...     

人工湿地污水处理技术及其在我国发展的现状与前景

人工湿地有着独具特色的净污机理、功能,正在不断地受到全球的重视。本文在国内外研究的基础上,简要介绍了人工湿地污水处理技术的机理、类型．对其优势与不足进行了系统的分析。作者对我国研究、应用现状和存在问题进行了阐述,认为我国发展人工湿地处理污水具有广阔的发展前景,对目前应当开展的工作提出了战略性的观点。     

用泻利盐矿和卤水制备硫酸镁工艺探究

以盐田泻利盐矿和卤水为原料,通过兑卤、热溶、结晶法制备硫酸镁。探究了各工艺因素对产物收率及纯度的影响,找到了较佳的工艺条件;在此基础上进行粗镁精制,得到纯度99%以上的硫酸镁。     

土地覆被及生态服务价值变化的多时间尺度模拟——以四川省渔子溪流域为例

根据渔子溪流域1986年和1994年的遥感影像土地覆被解译资料为基础,建立了模拟渔子溪流域生态环境变化的Markov Chain模型和Patch-dynamics模型,并以2002年遥感影像资料作为验证数据对模型进行了误差分析。以此为基础,分别以1年和8年的时间尺度对渔子溪流域土地覆被及其生态服务价值的变化进行了模拟,结果表明,时间尺度对模拟结果具有显著影响：在1年和8年的模拟尺度下,以Markov Chain模型得到的流域生态服务价值在1986~2018年间变化的相对误差为20%;不同土地覆被类型在不同步长下模拟结果的相对误差不一样,表明不同土地覆被类型变化的特征时间尺度不一样,以Patch-dynamics模型的模拟结果为例,步长为1年比步长为8年的相对误差大的土地覆被类型为耕地 (-8.2%/-5.6%)、有林地 (-0.5%/-0.4%)、草地 (0.7%/0.4%) 和居民建设用地 (-29.9%/-16.4%),它们的年际变化较大,其变化趋势不稳定,受到人为偶然因素的影响明显;而相对误差较小的土地覆被类型为灌木林 (-1.5%/-1.7%) 和永久冰雪覆盖 (27.3%/41.9%),它们的变化趋势较为稳定,主要受比较稳定的自然因素的影响。研究还表明,采用Markov Chain模型的模拟结果与采用Patch-dynamics模型的模拟结果总体上是一致的,但后者的模拟结果更稳定、更可靠。     

Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS

Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system (GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period (May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters. Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient of determination (r ² ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater augmentation techniques need to be implemented to protect depleting groundwater resources.     

Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite,Nunavut, Canada

This paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of the RF technique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the known Au occurrences than the KD gold prospectivity map. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.     

Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping,and Introduction to the Special Issue of Papers in These Fields

In its 26 years of existence, the journal of Natural Resources Research (NRR) has published and continues to publish papers on geochemical anomaly and mineral potential mapping. This is consistent with its aims and scope to publish quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Over the years, NRR has contributed significantly more to the publication of developments in mineral potential mapping and notably less to the publication of developments in geochemical anomaly mapping. In more detail, NRR has contributed significantly more to the publication of research on development of robust quantitative methods for analysis and synthesis of spatial evidence of mineral potential but notably less to the publication of research on development of geologically focused models of mineral potential. The editorship of NRR recognizes the latter as a challenge to promote further research on development of numerically robust as well as geologically focused mineral potential models, and this special issue is a major initiative in response to that challenge. The recent inclusion of Natural Resources Research for coverage by the Clarivate Analytics (formerly the Institute for Scientific Information) in the Science Citation Index Expanded? and Journal Citation Reports^® (JCR) Science Edition will help make Natural Resources Research meet that challenge.