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The mineral system modeling approach for prospectivity mapping is an efficient and economic method to assess undiscovered mineral potential quantitatively. It is a procedure of modeling, acquiring, and coupling the proxies of footprints of mineral systems at multiple scales (e.g., regional, district, and deposit scales). In this approach, the critical issue from multiple scales is that the data collected are asymmetrical from the superficial to the deep or from mine to its brown fields, so that it is hard to employ and integrate them. To complete this study, firstly, multi-tactic 3D geological modeling methods, including the explicit, the implicit, and inversion, were used to build geological models in the condition of asymmetrical datasets at the deposit and district scales. Secondly, indicators acquired in drill-intensive fields among multisource datasets composed of geology, geochemistry, geophysics and alteration data were transferred to studies in deep and brown fields. Finally, deep (~ 1,100 m) and circumjacent potentials of mine were targeted in the Haoyaoerhudong gold deposit situated in the Urad Middle Banner area, Inner Mongolia, which is one of the largest black-rock-series-type gold mines in China. This proposed procedure is more visual, clear, intuitive, and transferable to drive mineral system approach to exploration discovery than previous GIS-based studies. [ABSTRACT FROM AUTHOR]

This paper reports an application of uncertainty visualisation of a regional scale (1:50 000) 3D geological geometry model to be involved in GIS-based 3D mineral potential assessment of the Xiangxibei lead-zinc mineral concentration area in northwestern Hunan District, China. Three-dimensional (3D) geological modelling is a process of interpretation that combines a set of input measurements in geometry. Today, technology has become a necessary part of GIS-based deep prospecting. However, issues of sparse data and imperfect understanding exist in the process so that there are several uncertainties in 3D geological modelling. And these uncertainties are inevitably transmitted into the post-processing applications, such as model-based mineral resource assessment. Thus, in this paper, first, a big-data-based method was used to estimate the uncertainty of a 3D geological model; second, a group of expectations of geological geometry uncertainty were calculated and integrated into ore-bearing stratoisohypse modelling, which is one of the major favourable parameters of assessment for Lead-Zinc (Pb-Zn) deep prospectivity mapping in northwestern Hunan; and finally, prospecting targets were improved. [ABSTRACT FROM AUTHOR]

Predictive modeling of mineral prospectivity, which integrates diverse evidence of ore-forming processes based on an ore deposit genetic model constitutes a significant task in mineral exploration targeting. Descriptive deposit models suffer from overwhelming dependence on the explicitly mappable characteristics of a given deposit used to simplify the complex mineral system, but this hinders mineral prospectivity modeling. This study proposes a hybrid approach that combines numerical modeling with machine learning to predict mineral prospectivity. This approach first uses the numerical modeling method to provide more insights and better characterize a complex mineral system. These comprise implicit footprints such as singular stress deformation, heat transmission and fluid flux inherent in the transient geodynamics of ore genesis. The approach then applies a machine learning algorithm as a data-fusion tool to integrate the simulated evidence to minimize uncertainty in the predictive modeling of mineral prospectivity, which fundamentally results from an incomplete understanding of the targeted mineral system. Finally, we use the Fankou Mississippi Valley type Pb–Zn deposit as an example to illustrate the application of the proposed hybrid method. The prospectivity modeling result is critically compared with that of the spatial modeling based on descriptive deposit modeling by means of measures of accuracy, area under the receiver operating characteristic curve, and normalized density of prediction–area plot. It is shown that the overall performance of the prospectivity map predicted using a hybrid method is significantly improved. As such, this study demonstrates that a hybrid framework combining numerical modeling with machine learning provides a useful and novel way of predicting mineral prospectivity. The proposed hybrid method embodies a pioneering paradigm that integrates physics and data, assimilating ore deposit genesis models and reconnaissance data for the prediction of mineral prospectivity in support of exploration targeting. This approach can be implemented to enhance exploration information systems through the predictive modeling of mineral prospectivity in a unified computing and decision-making environment. The hybrid framework combining numerical modeling and machine learning for predictive mapping of mineral prospectivity. [Display omitted] • Here we apply numerical modeling in mineral prospectivity mapping (MPM). • Numerical modeling integrates ore deposit genesis models and exploration data. • Numerical modeling places physical and chemical constrains on MPM. • Numerical modeling-constrained MPM outperforms traditional MPM. [ABSTRACT FROM AUTHOR]

In order to comprehensively utilize regional-scale geological, geochemical and geophysical datasets for future exploration of undiscovered porphyry Cu–Mo polymetallic deposits (PCMPDs) in the Chinese Eastern Tianshan orogenic belt, three data-driven mineral prospectivity mapping (MPM) methods, namely ordinary weights of evidence (WofE), fuzzy weights of evidence (FWofE) and logistic regression (LR), were employed to integrate these datasets for mapping prospectivity of undiscovered PCMPDs. Firstly, the geological setting and mineralization of PCMPDs in the Eastern Tianshan district are reviewed. Then, spatial datasets based on geological maps, stream sediment geochemical data, and Bouguer gravity and aeromagnetic data are introduced, and on the basis of the prospecting model for PCMPDs, layers of structural, lithological, geophysical and geochemical evidences are constructed using the spatial datasets by means of GIS-based techniques. Finally, these evidential layers were integrated by using the WofE, FWofE and LR methods to obtain posterior probability maps of PCMPDs and the results are critically compared. The main conclusions are that: (1) the porphyry Cu–Mo mineralization in the Eastern Tianshan was occurred in the subduction boundary of the Late Paleozoic Dananhu-Dacaotan arc system of Kanguertag-Huangshan deep fault belt. This geological inference is supported by all the data-driven MPM methods; (2) the conditional independence assumption for both WofE and FWofE can be easily violated in practical applications. This issue seems very difficult to be circumvented due to geological correlations of evidence layers; (3) the uncertainty of the LR modeling approach particularly with respect to models using multiclass response variables mainly arises from over-fitting of the (ln-transformed) linear relationship; and (4) if there is no need for estimation of the number of undiscovered PCMPDs, the prospectivity map biasedly estimated by either WofE or FWofE modeling can be recommended for targeting new exploration areas with more detailed reconnaissance of potential undiscovered PCMPDs. Otherwise, the prospectivity map unbiasedly estimated by LR modeling with binary evidence modeling approach can be priority of use. [ABSTRACT FROM AUTHOR]

In this paper, we constructed an exploration target model for volcanogenic massive sulfide and hydrothermal deposits in Honghai district (China) using moderate resolution ETM+, ASTER, and hyperspectral Hyperion images and high resolution ZY-3 images, and weights-of-evidence (WofE) analysis and concentration-area (C-A) fractal modeling. The methodology and mapping steps were: (1) ETM + images were used to extract hydroxyl and iron-oxide alterations for identification of linear and ring fault structures and prospective zones in regional scale; (2) ASTER images were used to extract SiO 2 index, kaolinite, chlorite, propylite, potassium, carbonate, and limonite alterations for identification of mineralization zones in district scale; (3) hyperspectral Hyperion images were analyzed to identify mineral components for identification of Cu Au deposit zones in district scale; (4) high resolution ZY-3 images were used to extract geological objects (e.g., volcanic rocks, integration, and linear and ring fault structures) and cross-validate multiple type alterations and their associations with lithological strata based on interpretation of ETM+ and ASTER images; (5) alteration/structural factors and geological objects were integrated for mineral prospectivity mapping by WofE analysis, and mineral prospectivity was classified by (C-A) fractal modeling. Ring faults, iron-oxide alteration, chlorite alteration and silicification are important exploration factors, whereas carbonate alteration, potassic alteration, and linear faults are secondary exploration factors. Ten exploration targets were recognized in the Honghai district. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]