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     

Enrichment Ratio-Tonnage Diagrams for Resource Assessment

The enrichment ratio (ER), defined as the ratio of grade of a metal element in a deposit to the crustal abundance of the metal, is proposed for assessing mineral resources. According to the definition, the enrichment ratio of a polymetallic deposit is given as a sum of enrichment ratios of all metals. The relation between ER and the cumulative tonnage integrated from the high ER side of about 4750 deposits in the world is approximated by the combination of three exponential functions crossing at ER values of 16 · 10³ and 600. High ER deposits are expected for the commodities Ag, Pb, and Au+Ag, and for epithermal, mesothermal, unconformity-related and vein types. In contrast, low ER deposits are typical for the commodities Cu, Mn, Mo, Ni, and U, and for chemically precipitated, Cyprus, laterite, orthomagmatic, pegmatite, placer, porphyry, and sandstone deposits. The critical ER value of the low ER class (the differential metal amount decreases with decreasing ER in the regions lower than the value) is 250 in all deposits, 610 in W+Mo, 2800 in Pb+Zn and 360 in Au+Ag, 530 in massive sulfides, 160 in the orthomagmatic type, 170 in placers, 220 in the porphyry type, 1900 in the replacement type, 580 in the stratabound type, 3400 in the unconformity-related type, and 1700 in vein type deposits. The frequency proportion determined by a keyword and a commodity provides valuable suggestions for mineral exploration: for example, the exploration target for chromite is a deposit characterized as orthomagmatic, whereas the expected commodity of a newly developed orthomagmatic deposit is chromite.     

Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.     

Ore value-tonnage diagrams for resource assessment

An ore value-tonnage diagram has been proposed for assessing mineral resources. Diagrams of W+Mo, and Pb+Zn deposits show a good linearity between ore value and logarithms of cumulative ore tonnage. Diagrams of the massive sulfide, orthomagmatic, placer, porphyry, replacement, and stratabound types are also linear. It is assumed, therefore, that deposits of each of these commodities and these types belong to a single population. In contrast, the ore value-tonnage relations of all the deposits analyzed here is approximated by the combination of two exponential functions. The same feature is seen for deposits of the Cu+W+Mo, Cu+Pb+Zn, and Au+Ag commodities, and of the vein and unconformity-related types. This suggests that deposits belonging to each of such categories are divided into the high and low value groups. We can expect, accordingly, to find high value deposits of such categories.     

Are Fractal Dimensions of the Spatial Distribution of Mineral Deposits Meaningful?

It has been proposed that the spatial distribution of mineral deposits is bifractal. An implication of this property is that the number of deposits in a permissive area is a function of the shape of the area. This is because the fractal density functions of deposits are dependent on the distance from known deposits. A long thin permissive area with most of the deposits in one end, such as the Alaskan porphyry permissive area, has a major portion of the area far from known deposits and consequently a low density of deposits associated with most of the permissive area. On the other hand, a more equi-dimensioned permissive area, such as the Arizona porphyry permissive area, has a more uniform density of deposits. Another implication of the fractal distribution is that the Poisson assumption typically used for estimating deposit numbers is invalid. Based on datasets of mineral deposits classified by type as inputs, the distributions of many different deposit types are found to have characteristically two fractal dimensions over separate non-overlapping spatial scales in the range of 5–1000 km. In particular, one typically observes a local dimension at spatial scales less than 30–60 km, and a regional dimension at larger spatial scales. The deposit type, geologic setting, and sample size influence the fractal dimensions. The consequence of the geologic setting can be diminished by using deposits classified by type. The crossover point between the two fractal domains is proportional to the median size of the deposit type. A plot of the crossover points for porphyry copper deposits from different geologic domains against median deposit sizes defines linear relationships and identifies regions that are significantly underexplored. Plots of the fractal dimension can also be used to define density functions from which the number of undiscovered deposits can be estimated. This density function is only dependent on the distribution of deposits and is independent of the definition of the permissive area. Density functions for porphyry copper deposits appear to be significantly different for regions in the Andes, Mexico, United States, and western Canada. Consequently, depending on which regional density function is used, quite different estimates of numbers of undiscovered deposits can be obtained. These fractal properties suggest that geologic studies based on mapping at scales of 1:24,000 to 1:100,000 may not recognize processes that are important in the formation of mineral deposits at scales larger than the crossover points at 30–60 km.     

Examining Risk in Mineral Exploration

Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin. Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.     

Geologic anomaly analysis for space-time distribution of mineral deposits in the middle-lower Yangtze area, southeastern China

A geologic anomaly is a geologic feature or structure that departs markedly from its surrounding environment with respect to composition, texture, or genesis. The analysis of geologic anomalies related to mineralization is based upon specific geologic factors and a combination of features, such as structural, temporal, and spatial, and draws upon special effects that are due to ore-forming processes. An analysis of the geologic anomalies in the, middle-lower Yangtze area in southeastern China has led to an interpretation of the relation between anomaly subtypes and the occurrence and spatial distribution of ore deposits. Consequently, the following conclusions have been reached: the type of anomaly reflects the controlling factors that led to the formation of iron, copper, and gold deposits in the area; sedimentary geologic anomalies are most closely associated with stratiform deposits; structral complexity anomalies are most closely associated with Cu−Fe−Au deposits; magmatic anomalies reflect geologic processes in which Fe and Cu elements were separated from magma and enriched into ores; and the geologic combination entropy anomaly is proposed as a comprehensive variable that is related to favorable ore-forming environments and that can serve as a quantitative index that can be used in mineral exploration.     

Supergene Mass-Balance Study Assuming Zero Lateral Copper Flux Using Geostatistics to Recognize Metal Source Zones in Exotic Copper Deposits

Recognition of effective factors that influence the spatial extension of supergene weathering zones is important both for the identification of high potential areas of exotic deposits and for the cost-effective planning of mining. In particular, recognition of exotic mineralization around porphyry copper deposits early in mine development prevents them from being buried beneath mine infrastructures such as waste dump and tailing structures. Mass-balance modeling, a practical method for determining high potential areas of undiscovered exotic mineralization, investigates important factors in forming exotic deposits. Mass-balance modeling is a two-phase methodology that becomes progressively more detailed. An initial result, presented here as phase 1, is based solely on Cu assays. Phase 2 incorporates relict sulfide mineral studies to improve phase 1 modeling results and computes actual fluxes of copper that escaped vertically downward from the leached cap to form the enrichment blanket and then flowed laterally away to form exotic mineralization. In addition, geostatistical approaches, especially sequential Gaussian simulation, are useful tools for investigating the spatial relationships and modeling of mass-balance results in phase 1 studies. This paper introduces a method for interpolation and downscaling of the preliminary mass-balance analysis (phase 1) to highlight the role of geological features in the evolution of the supergene process. Using only copper assays without any need for relict sulfide mineralogy, this approach can be used to approximately identify the geographic direction of metal movement in exotic copper deposits, and thus serve as an initial exploration guide in prospecting for exotic deposits. For this, a vertical columnar block model was constructed for each of the supergene weathering zones and preliminary analysis of mass balance was conducted to reconstruct the apparent total leached zone column height assuming zero lateral flux. This analysis was applied to each of the vertical block model columns. The results of mass balance were interpolated in a 5?×?5 m grid by sequential Gaussian simulation method, and the simulated surface of the total leached zone was conflated with geological features. The roles of topography, argillic alteration and linear structures were identified in the transport of supergene solutions in the Miduk porphyry copper deposit of Iran. In the northern section of the deposit, which is in accordance with the topography gradient and the presence of advanced argillic alteration zone, the computed top total of leaching is below the actual surface topography, whereas the hypogene isograd curves confirm the expansion of primary copper in these areas. The northern section of the deposit was introduced as a susceptible area for the removal of copper-bearing solutions from the supergene enrichment system.

     

Geological Modeling of Gold Deposit Based on Grade Domaining Using Plurigaussian Simulation Technique

Mineral resource evaluation requires defining grade domains of an ore deposit. Common practice in mineral resource estimation consists of partitioning the ore body into several grade domains before the geostatistical modeling and estimation at unsampled locations. Many ore deposits are made up of different mineralogical ensembles such as oxide and sulfide zone: being able to model the spatial layout of the different grades is vital to good mine planning and management. This study addresses the application of the plurigaussian simulation to Sivas (Turkey) gold deposits for constructing grade domain models that reproduce the contacts between different grade domains in accordance with geologist’s interpretation. The method is based on the relationship between indicator variables from grade distributions on the Gaussian random functions chosen to represent them. Geological knowledge is incorporated into the model by the definition of the indicator variables, their truncation strategy, and the grade domain proportions. The advantages of the plurigaussian simulation are exhibited through the case study. The results indicated that the processes are seen to respect reproducing complex geometrical grades of an ore deposit by means of simulating several grade domains with different spatial structure and taking into account their global proportions. The proposed proportion model proves as simple to use in resource estimation, to account for spatial variations of the grade characteristics and their distribution across the studied area, and for the uncertainty in the grade domain proportions. The simulated models can also be incorporated into mine planning and scheduling.     

Where Are Porphyry Copper Deposits Spatially Localized? A Case Study in Benguet Province,Philippines

To provide guides for exploration of porphyry copper mineralization at a district scale, we examine the spatial association between known porphyry copper deposits and geologic features in Benguet, Philippines. The spatial associations between the porphyry copper deposits and strike-slip fault discontinuities, batholithic pluton margins and porphyry plutons are quantified using weights of evidence modeling. In the training and testing district, the porphyry copper occurrences are associated spatially with strike-slip fault discontinuities, batholithic pluton margins and contacts of porphyry plutons within distances of 3 km, 2.25 km, and 1 km, respectively. In addition, the porphyry plutons are associated spatially with strike-slip fault discontinuities and contacts of batholithic plutons within a distance of 2.25 km and 3 km, respectively. Based on these significant spatial associations, predictive maps are generated to delineate zones favorable for porphyry copper mineralization and zones favorable for emplacement of porphyry plutons in Benguet province, Philippines. Validations of the predictive models demonstrate their efficacy in pointing to zones for subsequent follow-up exploration work.     

Zonation in primary halos and geochemical prospecting pattern for the Guilaizhuang gold deposit, eastern China

The Guilaizhuang gold deposit, with an average grade of 8.10 g/t Au and reserves of over 30 mt, is a subvolcanic epithermal deposit. The deposit is hosted in Paleozoic carbonate rocks in the western Shandong metallogenic terrane of the littorine Pacific metallogenic domain, eastern China, and is associated spatially with an early Mesozoic subvolcanic alkalic intrusive complex (188–190 Ma). The orebody was discovered at the end of the 1980s based on anomalies of Au in stream sediment samples at a map scale of 1∶200,000. The ore is rich in Au, Ag, Te, V, F, As, Sb, Tl, W, and Mo but poor in Cu, Pb, and Zn. The ore is similar in its trace elements to Carlin-type Au deposits. The transverse element association zonation of the deposit is as follows: (on the hanging wall) F⟸ W−Mo−As−Tl⟸Se−Sb−Bi⟸Au−Ag−Te (orebody) ⟹ Se−Sb−Bi (on the foot wall). The axial zonation is as follows: Au⟹Ag⟹Sb⟹V⟹Zn ⟹W⟹F⟹Mo⟹Tl⟹As. Indexes such as (Au+Sb) _d / (As+Tl) _d and (Au+ Ag) _d /(As+Tl) _d decrease with depth but dramatically increase at the level where the orebody pinches out, which indicates another orebody might exist at depth. Multivariate statistical analysis suggests that the ore (halo)-forming process can be divided into two stages: alteration and mineralization. The former includes: potash feldsparization, albitization, silicification, and fluoritization. The latter includes the following substages: arsenopyritization and scheelitization; pyritization, chalcopyritization, and sphaleritization; and native gold, electrum, and calaveritization. The last substage is considered to be the main ore-forming stage in the formation of the deposit.     

Knowledge Recovery for Continental-Scale Mineral Exploration by Neural Networks

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.     

Map Scale Effects on Estimating the Number of Undiscovered Mineral Deposits

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.     

Analysis of Zoning Pattern of Geochemical Indicators for Targeting of Porphyry-Cu Mineralization: A Pixel-Based Mapping Approach

In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies. The pixel-based method is a discrete field modeling of geochemical landscapes for mapping lithogeochemical anomalies. In this method, the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from. In addition to the pixel-based method, because delineation of mineral exploration target areas using geochemical data is a challenging task, the application of metal zoning concept is demonstrated for vectoring into porphyry mineralization systems. In this regard, different geochemical signatures of the deposit-type sought were mapped in a model. Application of the proposed pixel-based method and the metal zoning concept is a powerful tool for targeting areas with potential for porphyry copper deposits.     

Multifractal Modeling of Worldwide and Canadian Metal Size-Frequency Distributions

The Pareto-lognormal frequency distribution, which can result from multifractal cascade modeling, previously was shown to be useful to describe the worldwide size-frequency distributions of metals including copper, zinc, gold and silver in ore deposits. In this paper, it is shown that the model also can be used for the size-frequency distributions of these metals in Canada which covers 6.6% of the continental crust. Like their worldwide equivalents, these Canadian deposits show two significant departures from the Pareto-lognormal model: (1) there are too many small deposits, and (2) there are too few deposits in the transition zone between the central lognormal and the upper tail Pareto describing the size-frequency distribution of the largest deposits. Probable causes of these departures are: (1) historically, relatively many small ore deposits were mined before bulk mining methods became available in the twentieth century, and (2) economically, giant and supergiant deposits are preferred for mining and these have strongest geophysical and geochemical anomalies. It is shown that there probably exist many large deposits that have not been discovered or mined. Although overall the samples of the size-frequency distributions are very large, frequencies uncertainties associated with the largest deposits are relatively small and it remains difficult to estimate more precisely how many undiscovered mineral deposits there are in the upper tails of the size-frequency distributions of the metals considered.

     

Rank-Size Statistical Assessments of Undiscovered Gold Endowment in the Bendigo and Stawell Zones (Victoria) and the Mossman Orogen (Queensland), Australia: Comparison with Three-Part Assessment Results

The Bendigo and Stawell zones in Victoria and the Mossman Orogen in north Queensland host numerous orogenic gold deposits and are likely to contain significant undiscovered gold resources. This paper discusses applications of Zipf’s law to estimate the scale of residual gold endowment in each of the regions. Testing various plausible scenarios on whether or not the largest deposit in each region has been discovered and its endowment adequately evaluated provided some measure of uncertainty of assessment results. The Bendigo and Stawell zones are estimated to host 12 undiscovered ore fields with >31 t (1 Moz) of contained gold and another 35 undiscovered ore fields with >10 t (0.32 Moz) of gold, containing in total 1600 t (51 Moz) of gold. The total residual orogenic gold endowment of the Mossman Orogen is estimated to be between 3 and 30 t of gold, contained in extensions of known deposits and up to six significant undiscovered gold ore fields each containing >1 t of gold. These estimates are comparable to results of recent three-part quantitative mineral resource assessments for those areas.     

Application of the Geo-Anomaly Unit Concept in Quantitative Delineation and Assessment of Gold Ore Targets in Western Shandong Uplift Terrain,Eastern China

A number of large and giant ore deposits have been discovered within the relatively small areas of lithospheric structure anomalies, including various boundary zones of tectonic plates. The regions have become the well-known intercontinental ore-forming belts, such as the circum-Pacific gold–copper, copper–molybdenum, and tungsten–tin metallogenic belts. These belts are typical geological anomalous areas. An investigation into the hydrothermal ore deposits in different regions in the former Soviet Union illustrated that the geologic structures of ore fields of almost all major commercial deposits have distinct features compared with the neighboring areas. These areas with distinct features are defined as geo-anomalies. A geo-anomaly refers to such a geologic body or a combination of bodies that their composition, texture–structure, and genesis are significantly different from those of their surroundings. A geo-anomaly unit (GU) is an area containing distinct features that can be delineated with integrated ore-forming information using computer techniques on the basis of the geo-anomaly concept. Herein, the GU concept is illustrated by a case study of delineating the gold ore targets in the western Shandong uplift terrain, eastern China. It includes: (1) analyses of gold ore-forming factors; (2) compilation of normalized regional geochemical map and extraction of geochemical anomalies; (3) compilation of gravitational and aeromagnetic tectonic skeleton map and extraction of gravitational and aeromagnetic anomalies; (4) extraction of circular and linear anomalies from remote-sensing Landsat TM images; (5) establishment of a geo-anomaly conceptual model associated with known gold mineralization; (6) establishment of gold ore-forming favorability by computing techniques; and (7) delineation and assessment of ore-forming units. The units with high favorability are suggested as ore targets.     

Statewide Estimates of Undiscovered Deposits of Gold,Silver, Copper,Lead, and Zinc

Estimates of the number of undiscovered deposits on a statewide basis offer a different perspective on the nation's undiscovered resources of gold, silver, copper, lead, and zinc. Mean estimates of the number of undiscovered deposits statewide were extracted from the estimates of undiscovered deposits nationwide. More than 50 undiscovered deposits are estimated to occur in Alaska, Arizona, Nevada, and Wisconsin. Estimating the number of undiscovered deposits statewide serves as a measure of a state's total remaining mineral resources in known conventional deposit types.     

Estimation of undiscovered deposits in quantitative mineral resource assessments—examples from Venezuela and Puerto Rico

Quantitative mineral resource assessments used by the United States Geological Survey are based on deposit models. These assessments consist of three parts: (1) selecting appropriate deposit models and delineating on maps areas permissive for each type of deposit; (2) constructing a grade-tonnage model for each deposit model; and (3) estimating the number of undiscovered deposits of each type. In this article, I focus on the estimation of undiscovered deposits using two methods: the deposit density method and the target counting method.In the deposit density method, estimates are made by analogy with well-explored areas that are geologically similar to the study area and that contain a known density of deposits per unit area. The deposit density method is useful for regions where there is little or no data. This method was used to estimate undiscovered low-sulfide gold-quartz vein deposits in Venezuela.Estimates can also be made by counting targets such as mineral occurrences, geophysical or geochemical anomalies, or exploration plays and by assigning to each target a probability that it represents an undiscovered deposit that is a member of the grade-tonnage distribution. This method is useful in areas where detailed geological, geophysical, geochemical, and mineral occurrence data exist. Using this method, porphyry copper-gold deposits were estimated in Puerto Rico.     

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.