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1.
《Ore Geology Reviews》2003,22(1-2):117-132
A data-driven application of the theory of evidential belief to map mineral potential is demonstrated with a redefinition of procedures to estimate evidential belief functions. The redefined estimates of evidential belief functions take into account not only the spatial relationship of an evidence with the target mineral deposit but also consider the relationships among the subsets of spatial evidences within a set of evidential data layer. Proximity of geological features to mineral deposits is translated into spatial evidence and evidential belief functions are estimated for the proposition that mineral deposits exist in a test area. The integrated maps of degrees of belief for the proposition that mineral deposits exist in a test area is classified into a binary mineral potential map. For the Baguio district (Philippines), the binary gold potential map delineates (a) about 74% of the training data (i.e., locations of large-scale gold deposits) and (b) about 64% of the validation data (i.e., locations of small-scale gold deposits). The results demonstrate the usefulness of a geologically constrained mineral potential mapping using data-driven evidential belief functions to guide further surficial exploration work in the search for yet undiscovered gold deposits in the Baguio district. The results also indicate the usefulness of evidential belief functions for mapping uncertainties in the geologically constrained integrated predictive model of gold potential.  相似文献   

2.
Data-driven prospectivity mapping can be undermined by dissimilarity in multivariate spatial data signatures of deposit-type locations. Most cases of data-driven prospectivity mapping, however, make use of training sets of randomly selected deposit-type locations with the implicit assumption that they are coherent (i.e., with similar multivariate spatial data signatures). This study shows that the quality of data-driven prospectivity mapping can be improved by using a training set of coherent deposit-type locations. Analysis and selection of coherent deposit-type locations was performed via logistic regression, by using multiple sets of deposit occurrence favourability scores of univariate geoscience spatial data as independent variables and binary deposit occurrence scores as dependent variable. The set of coherent deposit-type locations and three sets of randomly selected deposit-type locations were each used in data-driven prospectivity mapping via application of evidential belief functions. The prospectivity map based on the training set of coherent deposit-type locations resulted in lower uncertainty, better goodness-of-fit to the training set, and better predictive capacity against a cross-validation set of economic deposits of the type sought. This study shows that explicit selection of training set of coherent deposit-type locations should be applied in data-driven prospectivity mapping.  相似文献   

3.
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   

4.
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data driven methods that are relatively less widely used in the mapping of mineral prospectivity, and thus have not been comparatively evaluated together thoroughly in this field.The performances of a series of MLAs, namely, artificial neural networks (ANNs), regression trees (RTs), random forest (RF) and support vector machines (SVMs) in mineral prospectivity modelling are compared based on the following criteria: i) the accuracy in the delineation of prospective areas; ii) the sensitivity to the estimation of hyper-parameters; iii) the sensitivity to the size of training data; and iv) the interpretability of model parameters. The results of applying the above algorithms to epithermal Au prospectivity mapping of the Rodalquilar district, Spain, indicate that the RF outperformed the other MLA algorithms (ANNs, RTs and SVMs). The RF algorithm showed higher stability and robustness with varying training parameters and better success rates and ROC analysis results. On the other hand, all MLA algorithms can be used when ore deposit evidences are scarce. Moreover the model parameters of RF and RT can be interpreted to gain insights into the geological controls of mineralization.  相似文献   

5.
In this study, a novel method that integrates C4.5 decision tree, weights-of-evidence and m-branch smoothing techniques was proposed for mineral prospectivity mapping. First, a weights-of-evidence model was used to rank the importance of each evidential map and determine the optimal buffer distance. Second, a classification technique that uses a C4.5 decision tree in data mining was used to construct a decision tree classifier for the grid dataset. Finally, an m-branch smoothing technique was used as a predictor, which transformed the decision tree into a probability evaluation tree. The method makes no conditional independence assumption and can be applied for class imbalanced datasets like those collected during mineral exploration for prospectivity mapping of an area. The traits of comprehensibility, accuracy and efficiency were derived from the C4.5 decision tree. In addition, a case study for iron prospectivity mapping was performed in the eastern Kunlun Mountains, China. Sixty-two Skarn iron deposits and eight evidential maps related to iron mineralization were studied. On the final map, areas of low, moderate and high potential for iron deposit occurrence covered areas of 71,491, 14,298, and 9,532 km2, respectively. For the goodness-of-fit test, 91.94 % of the total 62 iron deposits were within a high-potential area, 8.06 % were within a moderate-potential area and 0 % were within a low-potential area. For ten-fold cross-validation, 82.26 % were within a high-potential area, 14.52 % were within a moderate-potential area and 3.22 % were within a low-potential area. To evaluate the predictive accuracy, Receiver Operating Characteristic (ROC) curves and Area Under ROC Curve (AUC) were employed. The accuracy of the goodness-of-fit test reached 97.07 %, and the accuracy of the ten-fold cross-validation was 95.10 %. The majority of the iron deposits were within high-potential and moderate-potential areas, which covered a small proportion of the study area.  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.
This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.  相似文献   

8.
This paper proposes that the spatial pattern of known prospects of the deposit‐type sought is the key to link predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA). This proposition is demonstrated by PMMP for hydrothermal Au‐Cu deposits (HACD) and by estimating the number of undiscovered prospects for HACD in Catanduanes Island (Philippines). The results of analyses of the spatial pattern of known prospects of HACD and their spatial associations with geological features are consistent with existing knowledge of geological controls on hydrothermal Au‐Cu mineralization in the island and elsewhere, and are used to define spatial recognition criteria of regional‐scale prospectivity for HACD. Integration of layers of evidence representing the spatial recognition criteria of prospectivity via application of data‐driven evidential belief functions results in a map of prospective areas occupying 20% of the island with fitting‐ and prediction‐rates of 76% and 70%, respectively. The predictive map of prospective areas and a proxy measure for degrees of exploration based on the spatial pattern of known prospects of HACD were used in one‐level prediction of undiscovered mineral endowment, which yielded estimates of 79 to 112 undiscovered prospects of HACD. Application of radial‐density fractal analysis of the spatial pattern of known prospects of HACD results in an estimate of 113 undiscovered prospects of HACD. Thus, the results of the study support the proposition that PMMP can be a part of QMRA if the spatial pattern of discovered prospects of the deposit‐type sought is considered in both PMMP and QMRA.  相似文献   

9.
In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used to construct the ELM regression model to map polymetallic prospectivity of the Lalingzaohuo district in Qinghai Province in China. Based on geologic, metallogenic, and statistical analyses of the study area, one target and eight predictor map patterns and two training sets were then used to train the ELM regression and logistic regression models. ELM regression modeling using the two training sets spends 61.4 s and 65.9 s; whereas the logistic regression modeling using the two training sets spends 1704.0 s and 1628.0 s. The four trained regression models were used to map polymetallic prospectivity. Based on the polymetallic prospectivity predicted by each model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was estimated. The ROC curves show that the two ELM-regression-based models somewhat dominate the two logistic-regression-based models over the ROC performance space; and the AUC values indicate that the overall performances of the two ELM-regression-based models are somewhat better than those of the two logistic-regression-based models. Hence, the ELM-regression-based models slightly outperform the logistic-regression-based models in mapping polymetallic prospectivity. Polymetallic targets were optimally delineated by using the Youden index to maximize spatial association between the delineated polymetallic targets and the discovered polymetallic deposits. The polymetallic targets predicted by the two ELM-regression-based models occupy lower percentage of the study area (2.66–2.68%) compared to those predicted by the two logistic-regression-based models (4.96%) but contain the same percentage of the discovered polymetallic deposits (82%). Therefore, the ELM regression is a useful fast-learning data-driven model that slightly outperforms the widely used logistic regression model in mapping mineral prospectivity. The case study reveals that the magmatic complexes, which intruded into the Baishahe Formation of the Paleoproterozoic Jinshuikou Group or the Carboniferous Dagangou and Shiguaizi Formations, and which were controlled by northwest-western/east-western trending deep faults, are critical for polymetallic mineralization and need to be paid much attention to in future mineral exploration in the study area.  相似文献   

10.
Data- and knowledge-driven techniques are used to produce regional Au prospectivity maps of a portion of Melville Peninsula, Northern Canada using geophysical and geochemical data. These basic datasets typically exist for large portions of Canada's North and are suitable for a “greenfields” exploration programme. The data-driven method involves the use of the Random Forest (RF) supervised classifier, a relatively new technique that has recently been applied to mineral potential modelling while the knowledge-driven technique makes use of weighted-index overlay, commonly used in GIS spatial modelling studies. We use the location of known Au occurrences to train the RF classifier and calculate the signature of Au occurrences as a group from non-occurrences using the basic geoscience dataset. The RF classification outperformed the knowledge-based model with respect to prediction of the known Au occurrences. The geochemical data in general were more predictive of the known Au occurrences than the geophysical data. A data-driven approach such as RF for the production of regional Au prospectivity maps is recommended provided that a sufficient number of training areas (known Au occurrences) exist.  相似文献   

11.
张士红 《地质与勘探》2020,56(2):239-252
四川省会理-会东矿集区是我国著名的铜资源基地。近年来,随着找矿勘查工作的深入,又提交了多处大中型铜矿床,表明该地区仍具有较大的找矿潜力。本文基于获取的地质、化探和物探数据,应用随机森林(Random Forest,RF)方法,在研究区开展"拉拉式"铜矿成矿潜力预测,取得了较好的效果:随机森林模型预测的平均袋外误差率为6. 25%,受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC)的AUC值为0. 938。利用偏依赖图(Partial Dependence Plot,PDP)分析解释了预测变量与已知矿床(点)的响应关系,按平均精度下降法排序预测要素的重要性,对"拉拉式"矿床的找矿预测工作具有重要意义的前4个变量依次为:Cu元素含量,中-晚元古代(超)基性岩体临近度,Ni元素含量和PC2因子得分。从成矿地质条件角度分析,河口群地层无疑是"拉拉式"铜矿的重要找矿预测要素,但在随机森林模型中的重要性排序相对靠后。究其原因,一方面是与其它连续数值型预测要素不同,河口群地层是二值(0～1)变量;另外,河口群地层的分布范围受覆盖层的影响较大。根据随机森林模型生成的拉拉地区成矿有利度信息,圈定了6处找矿远景区。红铜山-落凼-红泥坡-姜驿高成矿有利度区带呈北北东向展布,主体上与研究区内重要的地质-地球化学-地球物理预测要素异常空间分布一致;其中蒿枝坝-落凼-红泥坡-姑鲁迷找矿远景区是本区已探明铜矿的主要分布区,其内还有进一步勘探的潜力;同时,该异常区呈半环形态,结合地质勘探揭示的变质火山岩厚度分布,及其西部受一组后期北北东向走滑断裂限制的特点,显示出古火山活动中心在西部,并可能存在被切割分离的另一半环异常,这为该地区后续地质研究和铜矿勘查指明了方向。  相似文献   

12.
Qualitative and quantitative knowledge about the spatial association between mineral occurrences and geological features are important in mineral potential mapping. Two existing methods for quantifying spatial association between mineral occurrences and curvilinear geological features are applied to the Baguio district of the Philippines. An experimental method is described and applied to the study area as well. The results of the three methods are highly similar, which demonstrates the effectiveness of the experimental method presented here for quantifying spatial association between mineral occurrences and curvilinear geological features. It is shown that gold occurrence in the Baguio district are strongly spatially associated with northeasterly trending faults/fractures rather than with northwesterly trending faults/fractures. It is also shown that the spatial association between the gold occurrence and older batholithic intrusives is stronger than the spatial association between gold occurrence and younger porphyry intrusives. These spatial geoinformation characteristics can be used as evidential data layers in GIS-based mineral potential mapping.  相似文献   

13.
Weights of evidence and logistic regression are two of the most popular methods for mapping mineral prospectivity. The logistic regression model always produces unbiased estimates, whether or not the evidence variables are conditionally independent with respect to the target variable, while the weights of evidence model features an easy to explain and implement modeling process. It has been shown that there exists a model combining weights of evidence and logistic regression that has both of these advantages. In this study, three models consisting of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression are compared with each other for mapping mineral prospectivity. The modified fuzzy weights of the evidence model retains the advantages of both the fuzzy weights of the evidence model and the logistic regression model; the advantages being (1) the predicted number of deposits estimated by the modified fuzzy weights of evidence model is nearly equal to that of the logistic regression model, and (2) it can deal with missing data. This method is shown to be an effective tool for mapping iron prospectivity in Fujian Province, China.  相似文献   

14.
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   

15.
This paper demonstrates a modeling procedure of mineral potential mapping based on singularity theory, and further presents an idea to look into metallogeny of Sn–Cu polymetallic deposits in southeastern Yunnan mineral district, China by applying a localized regression method. Mineralization is a typical cascade process generally accompanied by irregular geological, geochemical and geophysical signatures. Singularity index as an efficient anomaly analytical tool helps to identify anomalies as well as characterize formation processes of these anomalies. In this study, the singularity-based mineral potential mapping method was utilized to characterize hydrothermal mineralization associated with magmatic, tectonic and sedimentary processes in this district. Based on the results, a mineral prospectivity model was constructed to delineate target areas. In addition to mineral prospectivity, controlling effects of geo-processes on mineralization are spatially non-stationary. Geographically-weighted regression analysis was thus employed to investigate these spatially-varied controlling effects and it has contributed to improve understanding to local metallogeny in the study area. Results of the spatial analysis presented can be used to guide following stages of mineral exploration in the district.  相似文献   

16.
This paper reports a deposit-scale GIS-based 3D mineral potential assessment of the Jiama copper-polymetallic deposit in Tibet, China. The assessment was achieved through a sequential implementation of metallogenic modelling and 3D modelling of geology, geochemistry and prospectivity. A metallogenic model for the Jiama deposit and 3D modelling workflow were used to construct multiple 3D layers of volumetric and triangular mesh models to represent geology, geochemistry and ore-controlling features in the study area. A GIS-based 3D weights-of-evidence analysis was then used to estimate the subsurface prospectivity for Cu (Mo) orebodies in the area, which led to the identification of three prospective deep-seated exploration targets. Additionally, the geochemical modelling indicates three potential fluid flow pathways based on the 3D zonation of major geochemical elements and their ratios, particularly the Zn/Pb ratios, which support the results of the weights of evidence model.  相似文献   

17.
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   

18.
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   

19.
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   

20.
Buffer zones in bo\th two-dimensional (2D) and three-dimensional (3D) spaces are commonly used in prospectivity mapping. The method completes a modelling that starts with a real example and progresses to the development of a virtual model. This includes the consideration of lithological or structural contacts at depth, which is a theoretical concept based on extrapolation of data collected in the field, rather than an empirical observation of the feature based on physical samples. This contribution documents an improved buffer analysis method for the study of 3D-space that is implicit (rapid), precise (smooth) and based on triangulated characteristics, which can be used to construct influence domains of geological models. As traditional 2D GIS-based mineral potential mapping is gradually becoming limited with time, mineral potential mapping in three dimensions (3D) is increasingly becoming an important tool in finding concealed economic mineralization. This contribution documents an improved methodology of buffer analysis for prospectivity mapping processing mineralized favourable models rather than describing an advance in the geometry of surface rendering of “geological complexity”. Measures used in this buffer analysis include the: (1) voxelization of geological objects (i.e. assigning numerical values of features on a regular cube in 3D-space); (2) revision of the 3D Euclidean distance transform and the calculation of signed distance field; (3) extracting surfaces from the field; and (4) construction of a buffer-surface based on a “discrete smooth interpolation” (DSI) algorithm. Furthermore, this contribution constructs 3D models using a buffer analysis algorithm and prospectivity mapping introduced here, which is based on real data from the Jiama Cu-polymetallic deposit in Tibet and Daye Fe deposit in the Hubei Province, China. This contribution also presents a comparison between voxel and irregular triangle models, which illustrate that irregular triangle mesh buffer analysis (ITB) can improve modelling techniques for GIS-based 3D mineral potential mapping. The outcome is an increase in the accuracy of prospectivity mapping.  相似文献   

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