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1.
Quantitative prediction and evaluation of mineral resources are one of the important topics of mathematical geology. On the basis of GIS technologies and weights of evidence modeling, MapGIS is integrated with GIS and mineral-resource prediction and evaluation. The final product is a predictor map of posterior probabilities of occurrence of the discrete event within a small unit cell. Predictor layers were created on a digital database that includes 1:200,000 scale geological, and geochemical, and geophysical maps, and remote-sensing images in study area. According to metallogenetic factors extractiont and weights of evidence modeling, there are four main metal ore belts in the study area: (1) the Batang belt; (2) the Lei Wuqi belt; (3) the Basu-Chayu belt; and (4) the Ganzi-Litang belt. The predictor map of posterior probabilities show that 29% of study area as zones with potential for porphyry copper, and 81% known mineral occurrences success rate is circled in the metallogenetic posterior probabilities map. The results demonstrate plausibility of weights-of-evidence modeling of mineral potential in large areas with small number of mineral prospects. 相似文献
2.
Seth S. Haines Jay E. Diffendorfer Laurie Balistrieri Byron Berger Troy Cook Don DeAngelis Holly Doremus Donald L. Gautier Tanya Gallegos Margot Gerritsen Elisabeth Graffy Sarah Hawkins Kathleen M. Johnson Jordan Macknick Peter McMahon Tim Modde Brenda Pierce John H. Schuenemeyer Darius Semmens Benjamin Simon Jason Taylor Katie Walton-Day 《Natural Resources Research》2014,23(1):3-17
Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development. 相似文献
3.
Estimates of numbers of undiscovered mineral deposits, fundamental to assessing mineral resources, are affected by map scale.
Where consistently defined deposits of a particular type are estimated, spatial and frequency distributions of deposits are
linked in that some frequency distributions can be generated by processes randomly in space whereas others are generated by
processes suggesting clustering in space. Possible spatial distributions of mineral deposits and their related frequency distributions
are affected by map scale and associated inclusions of non-permissive or covered geological settings. More generalized map
scales are more likely to cause inclusion of geologic settings that are not really permissive for the deposit type, or that
include unreported cover over permissive areas, resulting in the appearance of deposit clustering. Thus, overly generalized
map scales can cause deposits to appear clustered. We propose a model that captures the effects of map scale and the related
inclusion of non-permissive geologic settings on numbers of deposits estimates, the zero-inflated Poisson distribution. Effects
of map scale as represented by the zero-inflated Poisson distribution suggest that the appearance of deposit clustering should
diminish as mapping becomes more detailed because the number of inflated zeros would decrease with more detailed maps. Based
on observed worldwide relationships between map scale and areas permissive for deposit types, mapping at a scale with twice
the detail should cut permissive area size of a porphyry copper tract to 29% and a volcanic-hosted massive sulfide tract to
50% of their original sizes. Thus some direct benefits of mapping an area at a more detailed scale are indicated by significant
reductions in areas permissive for deposit types, increased deposit density and, as a consequence, reduced uncertainty in
the estimate of number of undiscovered deposits. Exploration enterprises benefit from reduced areas requiring detailed and
expensive exploration, and land-use planners benefit from reduced areas of concern. 相似文献
4.
5.
D. C. MASON D. G. CORR A. CROSS D. C. HOGG D. H. LAWRENCE M. PETROU 《International journal of geographical information science》2013,27(3):195-215
Abstract Remotely-sensed data constitute a major potential source of input to geographical information systems (GIS)However, these data often have a relatively poor classification accuracy compared with that of the cartographic data from maps with which they may be combined in the course of GIS analysis. The possibility exists of using data sets (in the form of digital maps) resident within a GIS in order to improve this accuracy, before the classified image is incorporated into the GIS. Results are discussed from a British Alvey Information Technology project to develop a system for the knowledge-based segmentation and classification of remotely-sensed terrain images, in which the knowledge contained in digital map 相似文献
6.
Geographical information system (GIS) techniques were used to investigate the spatial association between metallic mineral sites and lithodiversity in Nevada. Mineral site data sets include various size and type subsets of about 5,500 metal-bearing occurrences and deposits. Lithodiversity was calculated by counting the number of unique geological map units within four sizes of square-shaped sample neighborhoods (2.5-by-2.5, 5-by-5, 10-by-10, and 20-by-20 km) on three different scales of geological maps (national, 1:2,500,000; state, 1:500,000; county, 1:250,000). The spatial association between mineral sites and lithodiversity was observed to increase with increasing lithodiversity. This relationship is consistent for (1) both basin-range and range-only regions, (2) four sizes of sample neighborhoods, (3) various mineral site subsets, (4) the three scales of geological maps, and (5) areas not covered by large-scale maps. A map scale of 1:500,000 and lithodiversity sampling neighborhood of 5-by-5 km was determined to best describe the association. Positive associations occurred for areas having >3 geological map units per neighborhood, with the strongest observed at approximately >7 units. Areas in Nevada with more than three geological map units per 5-by-5 km neighborhood contain more mineral sites than would be expected resulting from chance. High lithodiversity likely reflects the occurrence of complex structural, stratigraphic, and intrusive relationships that are thought to control, focus, localize, or expose mineralization. The application of lithodiversity measurements to areas that are not well explored may help delineate regional-scale exploration targets and provide GIS-supported mineral resource assessment and exploration activity another method that makes use of widely available geological map data. 相似文献
7.
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping 总被引：5，自引：0，他引：5
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. 相似文献
8.
Mineral potential within the Greater Nahanni Ecosystem (GNE) was modelled in a Geographic Information System (GIS) for four
different deposit types: (1) SEDEX (stratiform shale-hosted sedimentary exhalative Zn–Pb–Ag), (2) ‘Carbonate-Fault’ (carbonate-hosted
zinc–lead–silver associated with major faults), (3) ‘Intrusion-Related’ (includes skarn, rare metals and gemstones) and (4)
Carlin-Type gold as lode and/or derived placer deposits. This mineral potential modelling study integrates data collected
during the Nahanni Mineral and Energy Resource Assessment (MERA) undertaken from 2003 to 2007. The results have contributed
to the process of determining the geographic boundaries of the proposed expansion of the Nahanni National Park Reserve. Four
mineral potential maps were produced (one for each deposit type) using a knowledge-driven approach. A weighting scheme based
on integrated mineral deposit and regional geological knowledge was derived for the various evidence maps for each deposit
model using expert opinion. The four potential maps were then combined into a final potential map using a maximum operator.
Plots showing the efficiency of the models (mineral potential maps) for predicting the known occurrences of the four deposit
types show that partial data sets provide reasonable predictions of the remaining known mineral prospects, occurrences and
deposits. Hydrocarbon potential from Nahanni MERA 1 was added to the final potential map to ensure that both mineral and energy
potential data were incorporated into the park configuration modelling. 相似文献
9.
Graeme P. McLaren 《Natural Resources Research》1992,1(1):85-96
British Columbia covers a vast segment of the Cordillera Mountain system that is richly endowed with a diversity of resources. British Columbia's historic patterns of resource development increasingly have been in conflict with demands for greater environmental protection. To avoid such conflicts, a recently legislated process that provides for detailed mineral resource assessments in candidate park areas has stimulated the creation of a mineral potential classification system for use in land-use planning and policy decisions.A mineral potential study of the Chilko Lake Planning Area provided three unique categories of field data on which to build the classification system. These categories are geological setting, geochemistry, and mineral occurrences. Data in each category were compiled independently to provide indicators of mineral potential. The field data were used to develop a widely understood classification of mineral potential. The classification is based on two factors: favorability and degree of confidence. 相似文献
10.
A personal computer-based geographic information system (GIS) is used to develop a geographic expert system (GES) for mapping and evaluating volcanogenic massive sulfide (VMS) deposit potential. The GES consists of an inference network to represent expert knowledge, and a GIS to handle the spatial analysis and mapping. Evidence from input maps is propagated through the inference network, combining information by means of fuzzy logic and Bayesian updating to yield new maps showing evaluation of hypotheses. Maps of evidence and hypotheses are defined on a probability scale between 0 and 1. Evaluation of the final hypothesis results in a mineral potential map, and the various intermediate hypotheses can also be shown in map form.The inference net, with associated parameters for weighting evidence, is based on a VMS deposit model for the Chisel Lake deposit, a producing mine in the Early Protoerzoic Snow Lake greenstone belt of northwest Manitoba. The model is applied to a small area mapped at a scale of 1:15,840. The geological map, showing lithological and alteration units, provides the basic input to the model. Spatial proximity to contacts of various kinds are particularly important. Three types of evidence are considered: stratigraphic, heat source, and alteration. The final product is a map showing the relative favorability for VMS deposits. The model is implemented as aFortran program, interfaced with the GIS. The sensitivity of the model to changes in the parameters is evaluated by comparing predicted areas of elevated potential with the spatial distribution of known VMS occurrences. 相似文献
11.
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. 相似文献
12.
Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines 总被引：9，自引：1，他引：9
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. 相似文献
13.
Mineral prospectivity mapping is an important preliminary step for mineral resource exploration. It has been widely applied to distinguish areas of high potential to host mineral deposits and to minimize the financial risks associated with decision making in mineral industry. In the present study, a maximum entropy (MaxEnt) model was applied to investigate its potential for mineral prospectivity analysis. A case study from the Nanling tungsten polymetallic metallogenic belt, South China, was used to evaluate its performance. In order to deal with model over-fitting, varying levels of β j -regularization were set to determine suitable β value based on response curves and receiver operating characteristic (ROC) curves, as well as via visual inspections of prospectivity maps. The area under the ROC curve (AUC = 0.863) suggests good performance of the MaxEnt model under the condition of balancing model complexity and generality. The relative importance of ore-controlling factors and their relationships with known deposits were examined by jackknife analysis and response curves. Prediction–area (P–A) curves were used to determine threshold values for demarcating high probability of tungsten polymetallic deposit occurrence within small exploration area. The final predictive map showed that high favorability zones occupy 14.5% of the study area and contain 85.5% of the known tungsten polymetallic deposits. Our study suggests that the MaxEnt model can be efficiently used to integrate multisource geo-spatial information for mineral prospectivity analysis. 相似文献
14.
15.
V.F. Rodriguez-Galiano M. Chica-Olmo M. Chica-Rivas 《International journal of geographical information science》2014,28(7):1336-1354
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. 相似文献
16.
Venkataraman G. Madhavan B. Babu Ratha D. S. Antony Joju P. Goyal R. S. Banglani S. Roy S. Sinha 《Natural Resources Research》2000,9(1):27-42
This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index. 相似文献
17.
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. 相似文献
18.
Bougrain Laurent Gonzalez Maria Bouchot Vincent Cassard Daniel Lips Andor L. W. Alexandre Frédéric Stein Gilbert 《Natural Resources Research》2003,12(3):173-181
This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data sets available in BRGM's GIS Andes (a comprehensive metallogenic continental-scale Geographic Information System) 25 attributes are identified as known factors or potential factors controlling the formation of gold deposits in the Andes Cordillera. Various multilayer perceptrons were applied to discriminate possible ore deposits from barren sites. Subsequently, because artificial neural networks can be used to construct a revised model for knowledge extraction, the optimal brain damage algorithm by LeCun was applied to order the 25 attributes by their relevance to the classification. The approach demonstrates how neural networks can be used efficiently in a practical problem of mineral exploration, where general domain knowledge alone is insufficient to satisfactorily model the potential controls on deposit formation using the available information in continent-scale information systems. 相似文献
19.
Abera Tessema 《Natural Resources Research》2017,26(4):465-488
Radial basis function link neural network (RBFLN) and fuzzy-weights of evidence (fuzzy-WofE) methods were used to assess regional-scale prospectivity for chromite deposits in the Western Limb and the Nietverdiend layered mafic intrusion of the Bushveld Complex in South Africa. Five predictor maps derived from geological, geochemical and geophysical data were processed in a GIS environment and used as spatial proxy for critical processes that were most probably responsible for the formation of the chromite deposits in the study area. The RBFLN was trained using input feature vectors that correspond to known deposits, prospects and non-deposits. The training was initiated by varying the number of radial basis functions (RBFs) and iterations. The results of training the RBFLN provided optimum number of RBFs and iterations that were used for classification of the input feature vectors. The results show that the network classified 73% of the validation deposits into highly prospective areas for chromite deposit, covering 6.5% of the study area. The RBFLN entirely classified all the non-deposit validation points into low prospectivity areas, occupying 86.6% of the study area. In general, the efficiency of the RBFLN in classifying the validation deposits and non-deposits indicates the degree of spatial relationship between the input feature vectors and the training points, which represent chrome mines and prospects. The RBFLN and fuzzy-WofE analyses used in this study are important in guiding identification of regional-scale prospect areas where further chromite exploration can be carried out. 相似文献
20.
论旅游地图编制内容更新的若干问题 总被引：3，自引：0，他引：3
本文是在《新疆维吾尔自治区旅游图》、《新疆与周边国家导游图》、《丝绸之路图》的编制实践中,结合目前市场上出版发行的十多种旅游地图进行比较分析,进而指出从中发现的问题,提出区域性旅游地图编制内容更新值得探讨的若干问题。 相似文献