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1.
In this study, both the fuzzy weights of evidence (FWofE) and random forest (RF) methods were applied to map the mineral prospectivity for Cu polymetallic mineralization in southwestern Fujian Province, which is an important Cu polymetallic belt in China. Recent studies have revealed that the Zijinshan porphyry–epithermal Cu deposit is associated with Jurassic to Cretaceous (Yanshanian) intermediate to felsic intrusions and faulting tectonics. Evidence layers, which are key indicators of the formation of Zijinshan porphyry–epithermal Cu mineralization, include: (1) Jurassic to Cretaceous intermediate–felsic intrusions; (2) mineralization-related geochemical anomalies; (3) faults; and (4) Jurassic to Cretaceous volcanic rocks. These layers were determined using spatial analyses in support by GeoDAS and ArcGIS based on geological, geochemical, and geophysical data. The results demonstrated that most of the known Cu occurrences are in areas linked to high probability values. The target areas delineated by the FWofE occupy 10% of the study region and contain 60% of the total number of known Cu occurrences. In comparison with FWofE, the resulting RF areas occupy 15% of the study area, but contain 90% of the total number of known Cu occurrences. The normalized density value of 1.66 for RF is higher than the 1.15 value for FWofE, indicating that RF performs better than FWofE. Receiver operating characteristics (ROC) were used to validate the prospectivity model and check the effects of overfitting. The area under the ROC curve (AUC) was greater than 0.5, indicating that both prospectivity maps are useful in Cu polymetallic prospectivity mapping in southwestern Fujian Province. 相似文献
2.
A Sugeno-type fuzzy inference system is implemented in the framework of an adaptive neural network to map Cu–Au prospectivity of the Urumieh–Dokhtar magmatic arc (UDMA) in central Iran. We use the hybrid “Adaptive Neuro Fuzzy Inference System” (ANFIS; Jang, 1993) algorithm to optimize the fuzzy membership values of input predictor maps and the parameters of the output consequent functions using the spatial distribution of known mineral deposits. Generic genetic models of porphyry copper–gold and iron oxide copper–gold (IOCG) deposits are used in conjunction with deposit models of the Dalli porphyry copper–gold deposit, Aftabru IOCG prospect and other less important Cu–Au deposits within the study area to identify recognition criteria for exploration targeting of Cu–Au deposits. The recognition criteria are represented in the form of GIS predictor layers (spatial proxies) by processing available exploration data sets, which include geology, stream sediment geochemistry, airborne magnetics and multi-spectral remote sensing data. An ANFIS is trained using 30% of the 61 known Cu–Au deposits, prospects and occurrences in the area. In a parallel analysis, an exclusively expert-knowledge-driven fuzzy model was implemented using the same input predictor maps. Although the neuro-fuzzy analysis maps the high potential areas slightly better than the fuzzy model, the well-known mineralized areas and several unknown potential areas are mapped by both models. In the fuzzy analysis, the moderate and high favorable areas cover about 16% of the study area, which predict 77% of the known copper–gold occurrences. By comparison, in the neuro-fuzzy approach the moderate and high favorable areas cover about 17% of the study area, which predict 82% of the copper–gold occurrences. 相似文献
3.
Mineral exploration programs commonly use a combination of geological, geophysical and remotely sensed data to detect sets of optimal conditions for potential ore deposits. Prospectivity mapping techniques can integrate and analyse these digital geological data sets to produce maps that identify where optimal conditions converge. Three prospectivity mapping techniques – weights of evidence, fuzzy logic and a combination of these two methods – were applied to a 32,000 km2 study area within the southeastern Arizona porphyry Cu district and then assessed based on their ability to identify new and existing areas of high mineral prospectivity. Validity testing revealed that the fuzzy logic method using membership values based on an exploration model identified known Cu deposits considerably better than those that relied solely on weights of evidence, and slightly better than those that used a combination of weights of evidence and fuzzy logic. This led to the selection of the prospectivity map created using the fuzzy logic method with membership values based on an exploration model. Three case study areas were identified that comprise many critical geological and geophysical characteristics favourable to hosting porphyry Cu mineralisation, but not associated with known mining or exploration activity. Detailed analysis of each case study has been performed to promote these areas as potential targets and to demonstrate the ability of prospectivity modelling techniques as useful tools in mineral exploration programs. 相似文献
4.
《Chemie der Erde / Geochemistry》2022,82(3):125898
Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (Nd) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (Nd = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (Nd = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study. 相似文献
5.
Renguang ZUO 《Resource Geology》2011,61(3):296-303
An exploration targeting model for Gangdese porphyry copper deposit in Tibet, China, is constructed based on (i) the age of porphyry intrusions within Gangdese magmatic arc; (ii) the regional‐scale normal E–W, N–S and N–E striking faults; and (iii) comprehensive anomalously high concentrations of Cu‐Mo‐Au‐Ag‐Pb‐Zn. These targeting elements are derived from geological map and geochemical dataset, and are integrated by weights of evidence with the aid of geographic information system (GIS). The resulting prospectivity for porphyry copper deposits delineated by posterior probability demonstrates that the target areas extend along the Yaluzangbujiang River and contain the two large deposits, Qulong and Chongjiang, located in the eastern and central part of the Gangdese belt, respectively. These results indicate that the proposed exploration targeting model is a potential tool to map regional‐scale mineral prospectivity. The target areas with high values of favorability, especially where high concentrations of Cu‐Mo‐Au‐Ag‐Pb‐Zn are present, are the potential areas for finding undiscovered porphyry copper deposits. 相似文献
6.
We present a mineral systems approach to predictive mapping of orogenic gold prospectivity in the Giyani greenstone belt (GGB) by using layers of spatial evidence representing district-scale processes that are critical to orogenic gold mineralization, namely (a) source of metals/fluids, (b) active pathways, (c) drivers of fluid flow and (d) metal deposition. To demonstrate that the quality of a predictive map of mineral prospectivity is a function of the quality of the maps used as sources of spatial evidence, we created two sets of prospectivity maps — one using an old lithologic map and another using an updated lithological map as two separate sources of spatial evidence for source of metals/fluids, drivers of fluid flow and metal deposition. We also demonstrate the importance of using spatially-coherent (or geologically-consistent) deposit occurrences in data-driven predictive mapping of mineral prospectivity. The best predictive orogenic gold prospectivity map obtained in this study is the one that made use of spatial evidence from the updated lithological map and spatially-coherent orogenic gold occurrences. This map predicts 20% of the GGB to be prospective for orogenic gold, with 89% goodness-of-fit between spatially-coherent inactive orogenic gold mines and individual layers of spatial evidence and 89% prediction-rate against spatially-coherent orogenic gold prospects. In comparison, the predictive gold prospectivity map obtained by using spatial evidence from the old lithological map and all gold occurrences has 80% goodness-of-fit but only 63% prediction-rate. These results mean that the prospectivity map based on spatially-coherent gold occurrences and spatial evidence from the updated lithological map predicts exploration targets better (i.e., 28% smaller prospective areas with 9% stronger fit to training gold mines and 26% higher prediction-rate with respect to validation gold prospects) than the prospectivity map based on all known gold occurrences and spatial evidence from the old lithological map. 相似文献
7.
Previous prospectivity modelling for epithermal Au–Ag deposits in the Deseado Massif, southern Argentina, provided regional-scale prospectivity maps that were of limited help in guiding exploration activities within districts or smaller areas, because of their low level of detail. Because several districts in the Deseado Massif still need to be explored, prospectivity maps produced with higher detail would be more helpful for exploration in this region.We mapped prospectivity for low- and intermediate-sulfidation epithermal deposits (LISEDs) in the Deseado Massif at both regional and district scales, producing two different prospectivity models, one at regional scale and the other at district-scale. The models were obtained from two datasets of geological evidence layers by the weights-of-evidence (WofE) method. We used more deposits than in previous studies, and we applied the leave-one-out cross validation (LOOCV) method, which allowed using all deposits for training and validating the models. To ensure statistical robustness, the regional and district-scale models were selected amongst six combinations of geological evidence layers based on results from conditional independence tests.The regional-scale model (1000 m spatial resolution), was generated with readily available data, including a lithological layer with limited detail and accuracy, a clay alteration layer derived from a Landsat 5/7 band ratio, and a map of proximity to regional-scale structures. The district-scale model (100 m spatial resolution) was generated from evidence layers that were more detailed, accurate and diverse than the regional-scale layers. They were also more cumbersome to process and combine to cover large areas. The evidence layers included clay alteration and silica abundance derived from ASTER data, and a map of lineament densities. The use of these evidence layers was restricted to areas of favourable lithologies, which were derived from a geological map of higher detail and accuracy than the one used for the regional-scale prospectivity mapping.The two prospectivity models were compared and their suitability for prediction of the prospectivity in the district-scale area was determined. During the modelling process, the spatial association of the different types of evidence and the mineral deposits were calculated. Based on these results the relative importance of the different evidence layers could be determined. It could be inferred which type of geological evidence could potentially improve the modelling results by additional investigation and better representation.We conclude that prospectivity mapping for LISEDs at regional and district-scales were successfully carried out by using WofE and LOOCV methods. Our regional-scale prospectivity model was better than previous prospectivity models of the Deseado Massif. Our district-scale prospectivity model showed to be more effective, reliable and useful than the regional-scale model for mapping at district level. This resulted from the use of higher resolution evidential layers, higher detail and accuracy of the geological maps, and the application of ASTER data instead of Landsat ETM + data. District-scale prospectivity mapping could be further improved by: a) a more accurate determination of the age of mineralization relative to that of lithological units in the districts; b) more accurate and detailed mapping of the favourable units than what is currently available; c) a better understanding of the relationships between LISEDs and the geological evidence used in this research, in particular the relationship with hydrothermal clay alteration, and the method of detection of the clay minerals; and d) inclusion of other data layers, such as geochemistry and geophysics, that have not been used in this study. 相似文献
8.
The Zhonggu iron orefield is one of the most important iron orefields in China, and is located in the south of the Ningwu volcanic basin, within the middle and lower Yangtze metallogenic belt of eastern China. Here, we present the results of new 3D prospectivity modeling that enabled the delineation of areas prospective for exploration of concealed and deep-seated Baixiangshan-type mineralization and Yangzhuang-type mineralization within the Zhonggu orefield; both of these deposits are Kiruna-type Fe-apatite deposits but are hosted by different formations within the Ningwu Basin. The modeling approach used during this study involves 3 steps: (1) combining available geological and geophysical data to construct 3D geological models; (2) generation of 3D predictive maps from these 3D geological models using 3D spatial analysis and 3D geophysical methods; (3) combining all of the 3D predictive maps using logistic regression to create a prospectivity map. This approach integrates a large amount of available geoscientific data using 3D methods, including 3D geological modeling, 3D/2D geophysical methods, and 3D spatial analysis and data integration methods. The resulting prospectivity model clearly identifies highly prospective areas that not only include areas of known mineralization but also a number of favorable targets for future mineral exploration. The 3D prospectivity modeling approach showcased within this study provides an efficient way to identify camp-scale concealed and deep-seated exploration targets and can easily be adapted for regional- and deposit- scale targeting. 相似文献
9.
The weights-of-evidence is a data-driven method that provides a simple approach to integration of diverse geo-data set information. In this study, we will use weights-of-evidence to build a model for predicting tracts in the Ahar–Arasbaran zone of Urumieh-Dokhtar orogenic belt (northwestern Iran) that are favorable for porphyry copper deposits. Weights of evidence are a data-driven method requiring known deposits and occurrences that are used as training points in the evaluated area. This zone hosts two major porphyry Cu deposits (The Sarcheshmeh deposit contains 450 million tonnes of sulfide ore with an average grade of 1.13 % Cu and 0.03 % Mo and Sungun deposit, which has 500 million tonnes of sulfide reserves grading 0.76 % Cu and 0.01 % Mo), and a number of subeconomic porphyry copper deposits are all associated with Mid- to Late Miocene diorite/granodiorite to quartz-monzonite stocks. Five evidential layers including geology, alteration, geochemistry, geophysics, and faulting are chosen for potential mapping. Weight factors were determined based on the applied method to generate last mineral prospectivity map. The studied area reduces to less than 11.78 %, while large zones are excluded for further studies. This result represents a significant area reduction and may help to better focus on mineral exploration targeting porphyry copper deposits in the Ahar–Arasbaran zone. 相似文献
10.
After almost five decades of episodic exploration, feasibility studies are now being completed to mine the deep-water nodular phosphate deposit on the central Chatham Rise. Weights of evidence (WofE) and fuzzy logic prospectivity models have been used in these studies to help in mapping of the exploration and resource potential, to constrain resource estimation, to aid with geotechnical engineering and mine planning studies and to provide background geological data for the environmental consent process. Prospectivity modelling was carried out in two stages using weights of evidence and fuzzy logic techniques. A WofE prospectivity model covering the area of best data coverage was initially developed to define the geological and environmental variables that control the distribution of phosphate on the Chatham Rise and map areas where mineralised nodules are most likely to be present. The post-probability results from this model, in conjunction with unique conditions and confidence maps, were used to guide environmental modelling for setting aside protected zones, and also to assist with mine planning and future exploration planning. A regional scale fuzzy logic model was developed guided by the results of the spatial analysis of the WofE model, elucidating where future exploration should be targeted to give the best chance of success in expanding the known resource. The development work to date on the Chatham Rise for nodular phosphate mineralisation is an innovative example of how spatial data modelling techniques can be used not only at the exploration stage, but also to constrain resource estimation and aid with environmental studies, thereby greatly reducing development costs, improving the economics of mine planning and reducing the environmental impact of the project. 相似文献
11.
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni. 相似文献
12.
13.
Fuzzy logic mineral prospectivity modelling was performed to identify camp-scale areas in western Victoria with an elevated potential for hydrothermal-remobilised nickel mineralisation. This prospectivity analysis was based on a conceptual mineral system model defined for a group of hydrothermal nickel deposits geologically similar to the Avebury deposit in Tasmania. The critical components of the conceptual model were translated into regional spatial predictor maps combined using a fuzzy inference system. Applying additional criteria of land use restrictions and depth of post-mineralisation cover, downgrading the exploration potential of the areas within national parks or with thick barren cover, allowed the identification of just a few potentially viable exploration targets, in the south of the Grampians-Stavely and Glenelg zones. Uncertainties of geological interpretations and parameters of the conceptual mineral system model were explicitly defined and propagated to the final prospectivity model by applying Monte Carlo simulations to the fuzzy inference system. Modelling uncertainty provides additional information which can assist in a further risk analysis for exploration decision making. 相似文献
14.
C. M. Knox-Robinson 《Australian Journal of Earth Sciences》2013,60(5):929-941
Modern exploration is a multidisciplinary task requiring the simultaneous consideration of multiple disparate geological, geochemical and geophysical datasets. Over the past decade, several research groups have investigated the role of Geographic Information Systems as a tool to analyse these data. From this research, a number of techniques has been developed that allow the extraction of exploration‐relevant spatial factors from the datasets. The spatial factors are ultimately condensed into a single prospectivity map. Most techniques used to construct prospectivity maps tend to agree, in general, as to which areas have the lowest and highest prospectivities, but disagree for regions of intermediate prospectivity. In such areas, the prospectivity map requires detailed interpretation, and the end‐user must normally resort to analysis of the original datasets to determine which conjunction of factors results in each intermediate prospectivity value. To reduce this burden, a new technique, based on fuzzy logic principles, has been developed for the integration of spatial data. Called vectorial fuzzy logic, it differs from existing methods in that it displays prospectivity as a continuous surface and allows a measure of confidence to be incorporated. With this technique, two maps are produced: one displays the calculated prospectivity and the other shows the similarity of input values (or confidence). The two datasets can be viewed simultaneously as a three‐dimensional perspective image in which colour represents prospectivity and topography represents confidence. With the vectorial fuzzy logic method, factors such as null data and incomplete knowledge can also be incorporated into the prospectivity analysis. 相似文献
15.
A major challenge for mineral exploration geologists is the development of a transparent and reproducible approach to targeting exploration efforts, particularly at the regional to camp scales, in terranes under difficult cover where exploration and opportunity costs are high. In this study, a three-pronged approach is used for identifying the most prospective ground for orogenic gold deposits in the Paleoproterozoic Granite-Tanami Orogen (GTO) in Western Australia.A key input to the analyses is the recent development of a 4D model of the GTO architectural evolution that provides new insights on the spatio-temporal controls over orogenic gold occurrences in the area; in particular, on the role of pre-mineralization (pre-1795 Ma) DGTOE–DGTO1–DGTO2 architecture in localization of gold deposits and the spatial distribution of rock types in 3D. This information is used to build up a model of orogenic gold minerals system in the area, which is then integrated into the three mutually independent but complementary mineral prospectivity maps namely, a concept-driven “manual” and “fuzzy” analysis; and a data-driven “automated” analysis.The manual analysis involved: (1) generation of a process-based gold mineral systems template to aid target selection; (2) manual delineation of targets; (3) manual estimation of the probability of occurrence of each critical mineralization process based on the available information; and (4) combining the above probabilities to derive the relative probability of occurrence of orogenic gold deposits in each of the targets. The knowledge-based Geological Information System (GIS) analysis attempts to replicate the expert knowledge used in the manual approach, but queried in a more systematic format to eliminate human heuristic bias. This involves representing the critical mineralization processes in the form of spatial predictor maps and systematically querying them through the use of a fuzzy logic model to integrate the predictor maps and to derive the western GTO orogenic gold prospectivity map. The data-driven ‘empirical’ GIS analysis uses no expert knowledge. Instead it employs statistical measures to evaluate the spatial associations between known deposits and predictor maps to establish weights for each predictor layer then combines these layers into a predictive map using a Weights of Evidence (WofE) approach.Application of a mineral systems approach in the manual analysis and the fuzzy analysis is critical: potential high value targets identified by these approaches in the western GTO lie largely under cover, whereas traditional manual targeting is biased to areas of outcrop or sub-crop amenable to direct detection technology such as exploration geochemistry, and therefore towards areas that are data rich.The results show the power of combining the three approaches to prioritize areas for exploration. While the manual analysis identifies and employs human intuition and can see through incomplete datasets, it is difficult to filter out human bias and to systematically apply to a large region. The fuzzy method is more systematic, and highlights areas that the manual analysis has undervalued, but lacks the intuitive power of the human mind that refines the target by seeing through incomplete datasets. The empirical WoE method highlights correlations with favorable host stratigraphy and highlights the control of an early set of structures potentially undervalued in the knowledge driven approaches, yet is biased due to the incomplete nature of exploration datasets and lack of abundant gold deposits due to the extensive cover.The results indicate that the most prospective areas for orogenic gold in western GTO are located in the central part of the study area, largely in areas blind to previous exploration efforts. According to our study, the procedure to follow should be to undertake the analyses in the following order: manual prospectivity analysis, followed by the conceptual fuzzy approach, followed by the empirical GIS-based method. Undertaking the manual analysis first is important to prevent explorationists from being biased by the automated GIS-based outputs. It is however emphasized that all of the prospectivity outputs from these three methods are possible, and they should not be treated as ‘treasure maps’, but instead, as decision-support aids. Therefore, a final manual prospectivity analysis redefined by the mutual consideration of output from all of the methods is required.The strategy employed in this study constitutes a new template for best-practice in terrane- to camp-scale exploration targeting that can be applied to different terranes and deposit types, particularly in terranes under cover, and provides a step forward in managing uncertainty in the exploration targeting process. 相似文献
16.
《地学前缘(英文版)》2020,11(6):2297-2308
Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential. More specifically, we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables, categorized into geological, geochemical and geophysical. On stochastically simulated sets of the multiple input layers, logistic regression is employed to produce different quantifications of the mineral potential in terms of probability. Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits. Additionally, we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty. Next, we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults, granite intrusions and sedimentary formations. Finally, we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential. Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian, China. 相似文献
17.
Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9–10???1; for the SVM model, a radial basis function was selected as the kernel function with best C?=?1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area. 相似文献
18.
宁芜盆地白象山矿区人工神经网络
三维成矿定位预测研究 总被引：2，自引：0，他引：2
本文基于三维地质环境,综合白象山矿区积累的地质资料和物探成果,首先开展三维地质建模工作,详细刻画了白象山矿区的三维地质结构;在三维地质模型基础上,利用三维空间分析手段对三维控矿因素进行定量挖掘,提取了多种三维控矿因素;最后采用人工神经网络方法进行三维成矿定位预测。预测结果显示,人工神经网络三维成矿定位预测能很好的定位出已知矿体,同时显示,在已知矿体北部及东部的深边部具有较高的成矿概率,可作为开展进一步找矿勘探的靶区。因此,人工神经网络三维成矿定位预测对于白象山矿区的应用是有效的,可服务于新老矿区的深边部三维成矿定位预测,同时可为隐伏矿、盲矿的成矿预测和优选靶区提供定量、定位新的方法和途径。 相似文献
19.
This paper describes the geology and tectonics of the Paleoproterozoic Kumasi Basin, Ghana, West Africa, as applied to predictive mapping of prospectivity for orogenic gold mineral systems within the basin. The main objective of the study was to identify the most prospective ground for orogenic gold deposits within the Paleoproterozoic Kumasi Basin. A knowledge-driven, two-stage fuzzy inference system (FIS) was used for prospectivity modelling. The spatial proxies that served as input to the FIS were derived based on a conceptual model of gold mineral systems in the Kumasi Basin. As a first step, key components of the mineral system were predictively modelled using a Mamdani-type FIS. The second step involved combining the individual FIS outputs using a conjunction (product) operator to produce a continuous-scale prospectivity map. Using a cumulative area fuzzy favourability (CAFF) curve approach, this map was reclassified into a ternary prospectivity map divided into high-prospectivity, moderate-prospectivity and low-prospectivity areas, respectively. The spatial distribution of the known gold deposits within the study area relative to that of the prospective and non-prospective areas served as a means for evaluating the capture efficiency of our model. Approximately 99% of the known gold deposits and occurrences fall within high- and moderate-prospectivity areas that occupy 31% of the total study area. The high- and moderate-prospectivity areas illustrated by the prospectivity map are elongate features that are spatially coincident with areas of structural complexity along and reactivation during D4 of NE–SW-striking D2 thrust faults and subsidiary structures, implying a strong structural control on gold mineralization in the Kumasi Basin. In conclusion, our FIS approach to mapping gold prospectivity, which was based entirely on the conceptual reasoning of expert geologists and ignored the spatial distribution of known gold deposits for prospectivity estimation, effectively captured the main mineralized trends. As such, this study also demonstrates the effectiveness of FIS in capturing the linguistic reasoning of expert knowledge by exploration geologists. In spite of using a large number of variables, the curse of dimensionality was precluded because no training data are required for parameter estimation. 相似文献
20.
Emmanuel John M. Carranza 《Resource Geology》2010,60(2):129-149
Wildcat modelling of mineral prospectivity has been proposed for greenfields geologically-permissive terranes where mineral targets have not yet been discovered but a geological map is available as a source of spatial data of predictors of mineral prospectivity. This paper (i) revisits the initial way of assigning wildcat scores (Sc) to predictors of mineral prospectivity and (ii) proposes an improvement by transforming Sc into improved wildcat scores (ISc) by using a logistic function. This was shown in wildcat modelling of prospectivity for low-sulphidation epithermal-Au (LSEG) deposits in Aroroy district (Philippines). Based on knowledge of characteristics of and controls on LSEG mineralization in the Philippines, the spatial predictors of LSEG prospectivity used in the study are proximity to porphyry plutonic stocks, faults/fractures and fault/fracture intersections. The Sc and ISc of spatial predictors are input separately to principal components analysis to extract a favourability function that can be interpreted as a wildcat model of LSEG prospectivity. The predictive capacity of the wildcat model of LSEG prospectivity based on the ISc of geological predictors is roughly 70% higher than that of the wildcat model of LSEG prospectivity based on the Sc of geological predictors. A slight increase of predictive capacity of wildcat modelling of LSEG prospectivity is also achieved when the ISc of geological predictors are integrated with the ISc of geochemical anomalies, but not with the Sc of geochemical anomalies. The proposed improvement is significant because if the study district were a greenfields exploration area, then a wildcat model of LSEG prospectivity based on the old wildcat methodology would have caused several LSEG targets to be missed. 相似文献