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1.
The aim of the present study is to analyze relationships between epithermal Au‐Ag deposits of the hydrothermal type and related geological factors and integrate the relationships using probabilistic and statistical models in a geographic information system (GIS) environment. A variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential Au and Ag deposits in the Gangreung area, Korea. This empirical approach assumes that all deposits shared a common genesis. The method consists of three main steps: (i) identification of spatial relationships; (ii) quantification of such relationships and (iii) integration of multiple quantified relationships. A spatial database containing Au and Ag deposits, topographic, geologic, geophysical and geochemical data was constructed using a GIS. The factors relating to 103 Au and Ag mineral deposits are the geological data such as lithology and fault structure, geochemical data including the abundance of Al, As, Ba, Ca, Cd, Co, conductivity, Cr, Cu, Eh, Fe, HCO3–, K, Li, Mg, Mn, Mo, Na, Ni, Pb, pH, Si, Sr, V, W, Zn, Cl?, F?, PO43?, NO2?, NO3? and SO42?, and geophysical data including Bouguer and magnetic anomalies. Using the constructed spatial database, the relationships between mineral deposit areas and 36 related factors are identified and quantified by probabilistic and statistical modeling; that is, likelihood ratio, weights of evidence and logistic regression. All the factors were combined to produce a map of the regional mineral potential using the overlay method in a GIS environment. The mineral potential map was then verified by comparison with known mineral deposits. The verification results give respective accuracies of 82.52%, 72.45% and 81.60% for the likelihood ratio, weights of evidence and logistic regression models, respectively. The mineral potential map can be used as a source of basic information for mineral resource development. 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.
《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
4.
Moung-Jin Lee Jae-Won Choi Hyun-Joo Oh Joong-Sun Won Inhye Park Saro Lee 《Environmental Earth Sciences》2012,67(1):23-37
Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model. 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.
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
7.
The Bayesian approach is an effective method of identifying the probability of mineralogical and geochemical type (MGT) mineralization of trace elements in galena, pyrite and other distributions in ore mineralization. Monomineralic samples have been identified using a computer-based Bayesian method and exploration geochemical techniques of Au deposits for MGT. In order to employ the method, a data bank was used consisting of the results of analysis of more than 12,000 monomineralic samples collected from the main hydrothermal Au deposits in Tajikistan (a territory of CIS). The Bayesian approach applied to geochemical data, such as posterior probabilities and discriminant analysis, provide numerical and graphical means through which the relationships between the trace elements and samples can be studied. The method used here, along with GIS, to find MGT can be used as geochemical indicators of regions with Au mineralization. The results of analyzing 100 monomineralic samples of pyrite from the Au–Ag Shkolnoe deposit (Tajikistan) show a multi-MGT anomaly superposition which is a combination of three MGT: (1) Au–Ag type (85% and more), (2) Au–sulfide-polymetallic type (46%), and (3) Au–sulfide type (40%). Mineralogical and geochemical maps (MGM) can be drawn based on results of MGT anomalies in a GIS environment. These maps can replace traditional metallogenic maps. The advantage of MGM substitutions is that a qualitative tool is replaced by a quantitative one. This helps one to make optimal managerial and more economical decisions. 相似文献
8.
The Haenam volcanic field was formed in the southern part of the Korean peninsula by the climactic igneous activity of the Late Cretaceous. The volcanic field hosts more than nine hydrothermal clay deposits and two epithermal Au–Ag deposits. This study focuses on the relationship between hydrothermal clay alteration and epithermal Au–Ag mineralization based on the geology, alteration mineralogy, geochronology, and mineralization characteristics.These clay and epithermal Au–Ag deposits are interpreted to have formed by the same hydrothermal event which produced two distinct types of mineral systems: 1) Au-dominant epithermal Au–Ag deposit and 2) clay-dominant hydrothermal clay deposit. The two types of mineral systems show a close genetic relationship as suggested by their temporal and spatial relationships. The Seongsan hydrothermal system progressively evolved from a low-intermediate sulfidation epithermal system with Au–Ag mineralization and phyllic alteration to an acid–sulfate high-sulfidation system with Au–Ag mineralization and/or barren advanced argillic/argillic alteration. The Seongsan system evolved during post volcanic hydrothermal activity for at least 10 Ma in the Campanian stage of the late Cretaceous.The Seongsan hydrothermal system shows the rare and unique occurrence of superimposed high to low (intermediate) sulfidation episodes, which persisted for about 10 Ma. 相似文献
9.
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
10.
化探异常信息识别是化探数据分析最重要的任务之一, 也是化探数据在资源勘查领域受到广泛关注的最重要原因, 前人对化探异常信息识别做过大量研究, 这些研究中的大多数主要关注化探示踪元素的含量, 近而根据含量指标计算异常阈值, 而对示踪元素在空间中的分布特征关注较少。本文选择 1: 20万比例尺的克拉玛依幅为研究区, 根据区内金矿的矿床地球化学特征选择Ag、As、Au和Sb等4种元素为本区内金矿的示踪元素, 以地球化学元素分散晕形成理论为依据, 使用GIS技术和Matlab软件绘制研究区内4种金矿示踪元素的综合地球化学异常图。结果表明, 与传统阈值方法得到的化探异常图相比, 本文得到的化探异常图能够更好地指示研究区内已知金矿。 相似文献
11.
Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images. 相似文献
12.
模糊证据权方法在镇沅(老王寨)地区金矿资源评价中的应用 总被引：11，自引：0，他引：11
采用模糊证据权方法和GeoDASGIS技术开展了镇沅(老王寨)及其邻区的金矿资源潜力评价.分别采用GeoDASGIS软件提供的局部奇异性分析技术、S-A异常分解技术、主成分分析技术、证据权、模糊证据权等技术对相关地球化学元素进行了系统的处理和分析.应用主成分分析方法确定了可能的2种不同成矿类型,并采用主成分得分确定了组合异常点,在此基础上分别采用普通证据权和模糊证据权方法编制了成矿后验概率图,圈定了有利成矿地段.对比普通证据权方法与模糊证据权方法所得结果表明,模糊证据权方法可减小图层离散化造成的有用信息损失,提高预测结果精度. 相似文献
13.
The relationship between major structural lineaments and locations of ore deposits in Iran has been investigated using geospatial data. In the course of lineament extraction, satellite images, aeromagnetic data, digital elevation model (DEM) and structural maps were processed and the lineaments and large-scale faults were identified. The extracted lineaments, based on subjective assessment, from each dataset were imported into GIS software and the “lineament map of Iran” was prepared by data integration. The analysis for selecting significant lineament was mainly based on fault correlated lineament and lineament with field map fractures, which was sets as benchmarks for compiling a final output map. Four major regional lineament trends of N–S, E–W, NW–SE and NE–SW were identified in the data of all images, which are corresponded to the structural zones and the major fault systems of Iran. The mineral deposits (active and abandoned) and mineral indications database compiled are based on the published maps, papers, reports and the ore deposits data files of Geological Survey of Iran. Integrating the output of these two datasets by GIS software resulted in the “Combined Map of Lineaments and Gold, Copper, Lead, Zinc and Iron Deposits of Iran”. The number and distance of ore deposits toward the lineaments were processed by the counting and cumulative methods in the GIS software's. Approximately, over 90% of the ore deposits of Iran are located in the central part of the lineaments (15 km on each side) which are concordant with a definition of large lineament. About 50% of these mineral deposits are closer than 5 km to the lineaments. There are significant correlations between lineament density and intersections with ore deposits occurrences. The observed associations at this scale are informative in establishing exploration strategy and decreasing exploration risks for detailed work on ore deposit scale. 相似文献
14.
Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model. 相似文献
15.
Yousef Ghanbari Ardeshir Hezarkhani Mohammad Ataei Kaveh Pazand 《Journal of the Geological Society of India》2014,83(4):457-465
One of the major strengths of a GIS is the ability to integrate and combine multiple layers of geoscience data for producing mineral potential maps showing favorable areas for mineral exploration. Once the data is prepared properly, the GIS, jointly with other statistical and geostatistical software packages, can be used to manipulate and visualize the data in order to produce a mineral prospectivity map. Many spatial modeling techniques can be employed to produce mineral potential maps. This paper demonstrates a technique to define favorable areas for REE mineralization with AHP technique using geological, geochemical, geophysical, alteration and faults density spatial data in the Kerman-Kashmar Tectonic Zone of central Iran. The AHP is a powerful and flexible multi-criteria decision-making tool for dealing with complex problems where both qualitative and quantitative aspects need to be considered. This approach is knowledgedriven method and can be applied in other areas for conventional use in mineral exploration. 相似文献
16.
《Chemie der Erde / Geochemistry》2021,81(4):125811
Geochemical exploration by stream sediment sampling using bulk leach extractable gold (BLEG) technique and applying concentration-number (C-N) fractal model, factor analysis (FA), and geochemical mineralization probability index (GMPI) resulted in the recognition of new Au occurrences around the Sukari gold mine in the central Eastern Desert of Egypt. The geochemical data of 128 stream sediment samples collected from the study area was used for delineating the geochemical anomalies and characterizing the dispersion trains of ore and associated elements (Au, Ag, As, Sb, Cu, Pb, Zn, Mo). Statistical analysis of the geochemical data applying the C-N fractal modeling enabled us to identify significant anomaly and background populations of the investigated elements and to construct reliable geochemical anomaly maps. Factor analysis using centered log-ratios (CLR), to address the problem of closed compositional data, revealed significant element associations for mineralization (Au, As, Mo, Zn, Ba), country rock compositions (Rb, Li, Be, Sn, Bi for granite, and Co, Cr, Ni for mafic rocks), and element mobility (e.g. Sb, Zr, and Ag). Weak and moderate Au anomalies that cannot be detected by factor score maps can be delineated clearly by using the C-N fractal method and GMPI distribution map. Our study revealed that Ag, As, and Sb are the main pathfinder elements for gold mineralization in arid to semiarid regions exemplified by the Sukari gold district. Silver can be used as a “direct” pathfinder, whereas As and Sb are “indirect” pathfinders for Au in such regions. The spatial distribution of Au and Ag anomalies indicate that gold mineralization in the Sukari district is structurally controlled. However, the spatial distribution of Cu, Pb, Zn, and Mo is controlled by mineralogical and lithological factors and is not related to any significant base metal deposits. 相似文献
17.
C. D'Ercole D. I. Groves C. M. Knox-Robinson 《Australian Journal of Earth Sciences》2013,60(5):913-927
Geographic Information Systems (GIS) provide an efficient vehicle for the generation of mineral prospectivity maps, which are products of the integration of large geological, geophysical and geochemical datasets that typify modern global‐scale mineral exploration. Conventionally, two contrasting approaches have been adopted, an empirical approach where there are numerous deposits of the type being sought in the analysed mature terrain, or a conceptual approach where there are insufficient known deposits for a statistically valid analysis. There are also a variety of potential methodologies for treatment of the data and their integration into a final prospectivity map. The Lennard Shelf represents the major Mississippi Valley‐type (MVT) province in Australia; however, there are only 13 deposits or major prospects known, making an empirical approach to prospectivity mapping impractical. Instead, a conceptual approach was adopted, where critical features that control the location of MVT deposits on the Lennard Shelf, as defined by widely accepted genetic models, were translated into features related to fluid pathways, depositional traps and fluid outflow zones, which can be mapped in a GIS and categorised as either regional or restricted diagnostic, or permissive criteria. All criteria were derived either directly from geological and structural data, or indirectly from geophysical and geochemical datasets. A fuzzy‐logic approach was adopted for the prospectivity analysis, where each interpreted critical feature of the conceptual model was assigned a weighting between 0 and 1 based on its inferred relative importance and reliability. The fuzzy‐logic method is able to cope with incomplete data, a common problem in regional‐scale exploration datasets. The data were best combined using the gamma operator to produce a fuzzy‐logic map for the prospectivity of MVT deposits on the southeastern Lennard Shelf. Five categories of prospectivity were defined. Importantly, from an exploration viewpoint, the two lowest prospectivity categories occupy ～90% and the highest two categories only 1.6% of the analysed area, yet eight of the 13 known MVT deposits lie in the latter and none in the former: i.e. all lie within ～10% of the area, despite the fact that deposit locations were not used directly in the analysis. The propectivity map also defines potentially mineralised areas in the central southeastern Lennard Shelf and the southern part of the Oscar Ranges, where there are currently no known deposits. Overall, the analysis demonstrates the power of fuzzy‐logic prospectivity mapping on a semi‐regional to regional scale, and emphasises the value of geological data, particularly accurate geological maps, in exploration for hydrothermal mineral deposits that formed late in the evolution of the terrain under exploration. 相似文献
18.
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
19.
王欣 《物探化探计算技术》2007,29(4):349-352
数据驱动的证据权法被用来进行金矿潜力制作。为了确定秦岭~松潘金矿的潜力区,需利用地质、地球化学、地球物理等数据。数据采集、图形处理、空间分析都是在GIS平台上进行的。预测结果表明,证据权法在综合不同空间数据上是有效的,最终的预测图件圈出了最有利的矿化区,可用于进一步勘查研究。 相似文献