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1.
The Random Forests (RF) algorithm has recently become a fledgling method for data-driven predictive mapping of mineral prospectivity, and so it is instructive to further study its efficacy in this particular field. This study, carried out using Baguio gold district (Philippines), examines (a) the sensitivity of the RF algorithm to different sets of deposit and non-deposit locations as training data and (b) the performance of RF modeling compared to established methods for data-driven predictive mapping of mineral prospectivity. We found that RF modeling with different training sets of deposit/non-deposit locations is stable and reproducible, and it accurately captures the spatial relationships between the predictor variables and the training deposit/non-deposit locations. For data-driven predictive mapping of epithermal Au prospectivity in the Baguio district, we found that (a) the success-rates of RF modeling are superior to those of weights-of-evidence, evidential belief and logistic regression modeling and (b) the prediction-rate of RF modeling is superior to that of weights-of-evidence modeling but approximately equal to those of evidential belief and logistic regression modeling. Therefore, the RF algorithm is potentially much more useful than existing methods that are currently used for data-driven predictive mapping of mineral prospectivity. However, further testing of the method in other areas is needed to fully explore its usefulness in data-driven predictive mapping of mineral prospectivity. 相似文献
2.
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
3.
大数据思维是直接从数据入手的一种新的思维方式,其本质是减少甚至完全屏蔽人为因素干扰,让数据说话。以往三维成矿预测中指标体系的建立多采用经验分析法,以地质模型和先验知识指导控矿特征变量取值,其准确性易受到人为影响。本文基于大数据思维,使用数据驱动方法对三维成矿预测中的找矿指标体系进行探索性研究,在钟姑矿田选择4 个大中型典型矿床,直接采用三维空间分析方法对地层和岩体的控矿地质体进行特征分析,通过计算z 轴方向三维距离场、分析岩体顶面隆起凹陷程度形态因素,确定各控矿要素与矿体之间的相关关系,获取定量指标。本研究改变了以往主观经验指导找矿的思路,尝试采用空间数据挖掘方法进行客观数据分析提出找矿指标,提高了找矿指标体系建立的科学性,此方法得到的定量指标体系可直接参与三维找矿预测模型的计算。 相似文献
4.
5.
《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
6.
Pravesh Debba Emmanuel J. M. Carranza Alfred Stein Freek D. van der Meer 《Mathematical Geosciences》2009,41(4):421-446
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
7.
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
8.
In the southwestern part of the Ashanti Belt, the results of fractal and Fry analyses of the spatial pattern of 51 known mines/prospects
of (mostly lode) gold deposits and the results of analysis of their spatial associations with faults and fault intersections
suggest different predominant structural controls on lode gold mineralisation at local and district scales. Intersections
of NNE- and NW-trending faults were likely predominantly involved in local-scale structural controls on lode gold mineralisation,
whilst NNE-trending faults were likely predominantly involved in district-scale structural controls on lode gold mineralisation.
The results of the spatial analyses facilitate the conceptualisation and selection of spatial evidence layers for lode gold
prospectivity mapping in the study area. The applications of the derived map of lode gold prospectivity and a map of radial
density of spatially coherent lode gold mines/prospects results in a one-level prediction of 37 undiscovered lode gold prospects.
The applications of quantified radial density fractal dimensions of the spatial pattern of spatially coherent lode gold mines/prospects
result in an estimate of 40 undiscovered lode gold prospects. The study concludes finally that analysis of the spatial pattern
of discovered mineral deposits is the key to a strong link between mineral prospectivity mapping and assessment of undiscovered
mineral deposits. 相似文献
9.
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
10.
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
11.
隐伏矿体三维综合信息成矿预测方法 总被引：6，自引：0，他引：6
开展三维综合信息成矿预测,是当前隐伏矿体找矿勘探的实际需要,但目前尚缺乏系统的研究,工作方法体系仍有待深入探讨。本文提出了一套较为完善的隐伏矿体三维综合信息定量预测流程和方法。方法包括数据收集及地质数据库管理、三维地质建模及地球物理数据融合、地质特征空间分析及控矿因素提取、多维多元控矿信息融合及预测信息集构建、隐伏矿体三维定位定量预测等多方面内容。由于方法综合了地质体三维建模、多维空间分析技术、地球物理方法以及预测方法,因此可有效提高三维成矿预测的有效性和可靠程度。为验证方法的有效性,本文在宁芜盆地分别针对矿田和矿区尺度,开展了中、大比例尺的三维成矿预测实例研究,取得了较好效果。研究显示该方法体系可有效地对深部隐伏矿体进行定位定量预测,能够服务于今后的新老矿区隐伏矿体找矿勘探工作。 相似文献
12.
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
13.
Emmanuel J. M. Carranza 《Resource Geology》2011,61(1):30-51
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
14.
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
15.
W. M. Brown T. D. Gedeon D. I. Groves R. G. Barnes 《Australian Journal of Earth Sciences》2013,60(4):757-770
A multilayer feed‐forward neural network, trained with a gradient descent, back‐propagation algorithm, is used to estimate the favourability for gold deposits using a raster GIS database for the Tenterfield 1:100 000 sheet area, New South Wales. The database consists of solid geology, regional faults, airborne magnetic and gamma‐ray survey data (U, Th, K and total count channels), and 63 deposit and occurrence locations. Input to the neural network consists of feature vectors formed by combining the values from co‐registered grid cells in each GIS thematic layer. The network was trained using binary target values to indicate the presence or absence of deposits. Although the neural network was trained as a binary classifier, output values for the trained network are in the range [0.1, 0.9] and are interpreted to indicate the degree of similarity of each input vector to a composite of all the deposit vectors used in training. These values are rescaled to produce a multiclass prospectivity map. To validate and assess the effectiveness of the neural‐network method, mineral‐prospectivity maps are also prepared using the empirical weights of evidence and the conceptual fuzzy‐logic methods. The neural‐network method produces a geologically plausible mineral‐prospectivity map similar, but superior, to the fuzzy logic and weights of evidence maps. The results of this study indicate that the use of neural networks for the integration of large multisource datasets used in regional mineral exploration, and for prediction of mineral prospectivity, offers several advantages over existing methods. These include the ability of neural networks to: (i) respond to critical combinations of parameters rather than increase the estimated prospectivity in response to each individual favourable parameter; (ii) combine datasets without the loss of information inherent in existing methods; and (iii) produce results that are relatively unaffected by redundant data, spurious data and data containing multiple populations. Statistical measures of map quality indicate that the neural‐network method performs as well as, or better than, existing methods while using approximately one‐third less data than the weights of evidence method. 相似文献
16.
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
17.
Saro Lee 《Environmental Earth Sciences》2013,70(7):3197-3215
The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W + and W ? of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis. 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.
隐伏矿体三维成矿定量预测是以多元地学大数据为基础的矿产预测新技术与方法。本文基于隐伏矿体三维成矿定量预测的实际需要及相关方法步骤,采用集成二次开发的方式,设计实现了一套可在三维环境下基于大数据开展定量化成矿预测工作的软件系统。本文阐述了系统的总体架构及开发方式,并对系统中各个功能模块的设计及实现过程进行详细阐述。系统融合了当前三维成矿定量预测研究的最新方法及成果,内含数据库管理、三维地球物理正演、三维空间分析以及三维预测评价等功能模块,能够对多元地学大数据进行集成和分析预测。为了验证系统的适用性和有效性,该系统被应用于长江中下游成矿带钟姑矿田三维成矿定量预测研究,相关成果表明系统的建立不但能够深化和发展三维成矿预测理论,也为新时期基于大数据的隐伏矿体找矿勘探工作提供了新方法及有力工具。 相似文献
20.
The main purpose of this study is to introduce a geographic information system (GIS)-based, multi-criteria decision analysis method for selection of favourable environments for Besshi-type volcanic-hosted massive sulphide (VHMS) deposits. The approach integrates two multi-criteria decision methods (analytical hierarchy process and ordered weighted averaging) and theory of fuzzy sets, within a GIS environment, to solve the problem of big suggested areas and missing known ore deposits in favourable environment maps for time and cost reduction. We doubled the fuzzy linguistic variables’ significance as a method to apply the arrange weights that the analytical hierarchy process (AHP)-ordered weighted averaging (OWA) hybrid procedure depends on. Another aim of this work is to assist mineral deposit exploration by modelling existing uncertainty in decision-making. Both AHP and fuzzy logic methods are knowledge-based, and they are affected by decision maker judgments. We used data-driven OWA approach in a hybrid method for solving this problem. We applied a new knowledge-guided OWA approach on data with changing linguistic variables according to the mineral system for VHMS deposits. Additionally, we used a vector-based method combination, which increased the precision of results. Results of knowledge-guided OWA showed that all of the mines and discovered deposits have been predicted with 100% accuracy in half of the size of the suggested area. To summarize, results improved the selection of possible target sites and increased the accuracy of results as well as reducing the time and cost, which will be used for field exploration. Finally, the hybrid methods with a knowledge-guided OWA approach have delivered more reliable results compared to exclusively knowledge-driven or data-driven methods. The study proved that expert knowledge and processed data (information) are critical important keys to exploration, and both of them should be applied in hybrid methods for reaching reliable results in mineral prospectivity mapping. 相似文献