Mineral Prospectivity Modeling Research Articles

A Global-Local collaborative approach to quantifying spatial non-stationarity in three-dimensional mineral prospectivity modeling

The three-dimensional (3D) modeling of mineral prospectivity plays a pivotal role in the mineral exploration process, enabling explorers to identify, evaluate, and prioritize deep-seated targets more effectively.Accurately predicting the 3D distribution of mineral prospectivity relies on effectively quantifying correlations to mineralization. The ore formation process is extremely intricate, marked by spatial non-stationary correlations. While state-of-the-art 3D mineral prospectivity modeling methods use machine learning (including deep learning) to capture correlation between geological evidence and mineralization hidden in the geosciences data, limited attention has been given to quantifying the local non-stationary nature in ore formation. This paper proposes a novel method that adapts to local spatial non-stationarity in 3D mineral prospectivity modeling. The approach integrates a prospectively model with a local regression model, creating a global–local collaborative model to quantify spatial non-stationary associations with mineralization. Random Forest (RF) and Reset are employed to construct mineral prospectivity models that quantify the global associations with mineralization. Simultaneously, the geographically weighted regression (GWR) model serves as a local model to capture the local non-stationarity within mineralization association. An Expectation Maximization algorithm is tailored to bridge the gap between global and local models, resulting in an iterative framework that progressively fuses global spatial associations and local non-stationarity related to mineralization. The iterative process generates a mineral perspectivity model that adapts to local non-stationarity in predictive areas. A case study on the Dingjiashan Pb-Zn deposit validates the effectiveness of the proposed method. The results demonstrate that the global–local collaborative mineral perspectivity model significantly boosts prediction accuracy in predictive areas. Moreover, the regression parameters of GWR derived from the model construction process effectively reveal the spatial non-stationary impacts of different ore-controlling factors, thereby improving the interpretability and reliability of predicted mineral prospectivity.

A novel hybrid ensemble model for mineral prospectivity prediction: A case study in the Malipo W-Sn mineral district, Yunnan Province, China

The utilization of machine learning and data-driven approaches for mineral prospectivity modeling has become increasingly important in supporting regional mineral exploration. However, supervised learning methods have encountered several common challenges, such as overfitting and underfitting, in the process of mineral prospectivity modeling, particularly due to imbalanced sample sizes used for model training. This imbalance could limit the generalization capability of models and result in inadequate predictive performance of individual models. The present study introduced a novel hybrid ensemble model (HEM) that integrates various learner methods, including generative adversarial network (GAN) and transfer learning techniques, to effectively address the issues above. These intelligent prediction techniques were applied to analyze the geological, geochemical, and geophysical dataset in order to delineate potential areas for W-Sn mineralization in Malipo County, Yunnan Province, China. Meanwhile, some well-established supervised learning methods such as Convolutional Neural Networks (CNN), Boosting-based model (e.g., LightGBM), and Bagging-based models, which demonstrated exceptional performance in dealing with classification issues were also integrated into the HEM. The weights for integrating these models were determined automatically using the equidistant-sampling technique. The superior performance of the HEM in identifying known deposits made it an efficient tool for mineral prediction mapping. Additionally, this model also offered high flexibility and scalability by allowing adjustments to the types and quantities of base learners based on data characteristics.

3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches

A significant proportion of recent mineral exploration has increasingly focused on the targeting of deep-seated orebodies. Mineral prospectivity modeling is one of the more important approaches that facilitates exploration targeting and the mitigation of risks associated with mineral exploration, particularly under cover. Recent advances in 3D mineral prospectivity modeling enable the effective extraction of predictive information from three-dimensional geological models, enabling more accurate exploration targeting of deep-seated orebodies. These advancements have synergized with deep learning approaches to improve the efficiency of mineral exploration based on nonlinear and multi-layer sensing attributes, effectively enabling the identification and extraction of key relationships between 3D predictive maps and mineralization. The main deep learning method used for 3D mineral prospectivity modeling is convolutional neural network (CNN) modeling. However, this research typically does not consider the multiscale features of geological structures, meaning further improvements can be made to this modeling approach. This paper introduces a multi-scale 3D convolutional neural network model (3D CNN) incorporating a spatial attention mechanism and an Inception module (MSAM-CNN) for 3D mineral prospectivity modeling. By integrating Inception modules and spatial attention mechanisms, the network's capability to identify multi-scale geological features and delineate key predictive areas is significantly enhanced compared to typical CNN approaches. This new approach provides further improvement in the accuracy and generalization capability of 3D mineral prospectivity modeling. To evaluate the effectiveness of this model, we undertook 3D mineral prospectivity modeling within the area of the Baixiangshan iron deposit, in the Ningwu Basin of the Middle-Lower Yangtze River Metallogenic Belt, China. The results show that the multi-scale 3D convolutional neural network model is remarkably robust and has good generalization capabilities. The approach can also can effectively delineate targets within the deep and peripheral areas of the deposit, providing targets for future exploration. The addition, performance indicators, ROC curve, and Capture-Efficiency curve generated during this modeling consistently demonstrate that the MSAM-CNN model outperforms Inception-enhanced CNN (M-CNN), CNN, Random Forest (RF), and Support Vector Machine (SVM) models. All of this indicates that MSAM-CNN approaches can effectively extract 3D spatial features within 3D predictive maps during 3D mineral prospectivity modeling better than other approaches that are commonly used, indicating that thius approach represents a promising tool for the accurate and precise identification of targets during future exploration for deep-seated mineralization.

Random forest-based mineral prospectivity modelling over the Southern Kibi–Winneba belt of Ghana using geophysical and remote sensing techniques

This study determines which predictors derived from geophysics or remote sensing data best generate a mineral prospectivity model (MPM) over Ghana's southern Kibi-Winneba belt in a scenario-based modeling case using Random Forest (RF) algorithm. Ten geophysically-derived predictors and six-remote sensing derived predictors were used as inputs in the first and second scenarios respectively. In the third case, the sixteen predictors derived from these afore-mentioned geoscientific datasets were used as inputs. Thus, three binary RF-based MPM were generated, and compared accordingly. The predictive performance in all three scenario-based RF-derived MPM produced was determined using the area under the receiver operating characteristic curve (AUC). AUC scores of 0.840, 0.785 and 0.809 respectively, were obtained for the first, second and third scenarios. The AUC scores obtained further indicates that, MPM developed based on using only the geophysics-sourced layers as inputs performed better in comparison with the MPMs generated in second and third scenarios.

Research on three-dimensional mineral prospectivity modeling by integrating numerical simulation of the ore-forming process: A case study in the Chating area of Xuancheng, Anhui province, China

Research on three-dimensional mineral prospectivity modeling by integrating numerical simulation of the ore-forming process: A case study in the Chating area of Xuancheng, Anhui province, China

3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism

Mineralization distribution is spatially heterogeneous and jointly controlled by multiple ore-controlling factors. Given this ubiquitous feature, we integrated the attention mechanism of the Convolutional Block Attention Module (CBAM), which contains both channel and spatial attention modules, into a convolutional neural network (CNN) to develop a more robust 3D mineral prospectivity modeling (MPM) approach. A network interpretive algorithm (DeepLIFT) was conducted to the CNN deconstruction to evaluate whether the key ore-controlling information is read. The Sanshandao goldfield in China, with significant fault controls on gold mineralization distribution, was selected as a case study to verify the proposed method. In this case, we generated the 5-channel images by projecting five ore-related fault features to feed into CNN. The optimal prospectivity model of CNN + CBAM was subsequently yielded by a gradient-based optimization of stochastic objective functions and an optimized regularization cross-entropy loss function. By decomposing the constructed model and the vanilla CNN model, we observed the CNN + CBAM has lower feature scores of fault distance and larger scores of fault geometry (e.g., dip and undulation) at Xiling, Haiyu, and Xinli than CNN. These segments occur significant fault strike or dip changes that have influenced mineralization spatial continuity, which is well consistent with the expert knowledge of metallogeny in the Sanshandao goldfield. This means that the CBAM has induced the CNN to focus on key ore-controlling features from complex exploration data, which is likely to improve the model’s prediction ability. The prospectivity results of CNN + CBAM and other comparison shallow machine learning methods (e.g., random forest) revealed that the CNN + CBAM model produced more reliable results, as evidenced by (1) being highly consistent with known mineralization, (2) having the highest AUC values and success rate, and (3) accurately predicting deep voxels that have been explored by drill holes. Thus, the proposed CNN + CBAM method, with a remarkable capacity for emphasizing critical ore-associated features, is recommended for application in deep 3D MPM. Six possible mineralization sites in the Sanshandao field were identified based on the CNN + CBAM prospective data, which may aid future gold exploration.

Combining numerical modeling and machine learning to predict mineral prospectivity: A case study from the Fankou Pb–Zn deposit, southern China

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.

A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran

A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran

A lightweight convolutional neural network with end-to-end learning for three-dimensional mineral prospectivity modeling: A case study of the Sanhetun Area, Heilongjiang Province, Northeastern China

With the continuous exploitation of surface and shallow mineral resources, the global demand for concealed ore deposit exploration is increasing. However, concealed mineral prospectivity modeling (MPM) requires significant efforts, particularly in terms of methods and technologies for three-dimensional modeling. In this study, we propose a lightweight three-dimensional convolutional neural network (3D CNN) for MPM, which adopts the inception structure of GoogleNet and combines the idea of end-to-end learning. We replace the fully connected layer in the conventional CNN with deconvolution, which can greatly reduce the required parameters during the training process and accelerate the convergence. The proposed method overcomes the disadvantage that other shallow machine learning methods cannot extract spatial neighborhood information, while it can extract cross-correlations among geological factors and generates less parameters by a lightweight network when facing massive data. Additionally, compared with the patch-wise method commonly used in previous studies, we use the pixel-wise method for end-to-end learning, which not only overcomes the drawbacks of random sampling but also considers the influence of each voxel when calculating the loss function. The three-dimensional multi-source geoscientific characteristics obtained from the geophysical inversion and 3D geological models are not discretized in order to promote effective CNN training while facilitating the ore-controlling representation. Comparing the predicted results between the 3D weight of evidence (WofE) and our proposed 3D CNN method for MPM, our proposed method and WofE delineated 100% of the known mineralization in high-favorability areas with voxel numbers of 70% and 95%, respectively. A case study of a structure-controlled hydrothermal gold deposit in the Sanhetun area of Heilongjiang Province demonstrates that the proposed 3D CNN method performs better than WofE in terms of prediction effectiveness and efficiency and effectively reveals the correlation between mineralization and adjacent ore-controlling characteristics. Moreover, the proposed 3D CNN method can simulate non-linear metallogenic processes and mine hidden relationships to reveal complex ore-controlling characteristics. In conclusion, the proposed 3D CNN method can reduce the exploration effort in 3D MPM, thus greatly improving the efficiency of discovering concealed ore deposits.

Three-Dimensional Mineral Prospectivity Modeling with Geometric Restoration: Application to the Jinchuan Ni–Cu–(PGE) Sulfide Deposit, Northwestern China

Three-Dimensional Mineral Prospectivity Modeling with Geometric Restoration: Application to the Jinchuan Ni–Cu–(PGE) Sulfide Deposit, Northwestern China

Mineral prospectivity modelling to delineate potential areas for gold deposits: a case study of Lupa Goldfield, South West Tanzania

ABSTRACT This study used the weight of evidence model to delineate prospective areas for gold deposits in the Lupa Goldfield. The method establishes a spatial relationship between known mineral deposits and evidential maps to create a mineral prospectivity map. Four evidential maps derived from geochemical, geological, geophysical, and topographical Digital Elevation Model (DEM) datasets were analysed and integrated to create a mineral prospectivity map. Results have revealed five classes ranked from the highest, high, medium, low, and lowest favourability patterns with probability values of 0.051, 0.0426, 0.0225, 0.0172, and 0.0091, respectively. The highest favourable areas have the best gold potentials based on the presence of predictor patterns from all four evidential maps. The posterior probability map revealed good prediction, existing gold deposits, such as Saza Mine and Shanta Gold Mine, were mapped. Results have shown that the weight of evidence model was successful and can be applied in mineral exploration targeting.

Three-Dimensional Mineral Prospectivity Modeling with the Integration of Ore-Forming Computational Simulation in the Xiadian Gold Deposit, Eastern China

Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We here conducted 3D mineral prospectivity modeling with the integration of ore-forming computational simulation information in the Xiadian orogenic gold deposit, China, to examine whether the simulation data input can improve the reliability of prospectivity modeling. First, we constructed the 3D models of the orebody and fault to extract the fault geometric features using spatial analysis, as they are always considered to be the crucial controls of gold distribution. Second, we performed 3D numerical modeling of the deformation–fluid–heat-coupling process of the structurally controlled hydrothermal Au system using the FLAC3D platform. Finally, the fault-geometry features (buffer, dip, dip variation, and undulation) and the ore-formation-simulation indices (volume strain, shear strain, temperature variation, and fluid flux) were integrated using Bayesian decomposition modeling, which has a promising nonlinear model ability and a flexible variable-integration ability. The prospectivity modeling results demonstrated that the model generated by combining geometry and simulation variables achieved significantly higher AUC, precision, accuracy, Kappa, and F1 scores compared to other models using a single-predictor-variable dataset. This suggests that the joint use of geometry and simulation variables construct a comprehensive association between gold and its ore-controlling factors, thereby resulting in a highly reliable prospectivity model. Thus, the approach of 3D mineral prospectivity modeling aided by ore-forming numerical simulation proves to be more useful in guiding mineral exploration, especially in the condition of fewer variables. Based on the prospectivity modeling outcomes, we identified four gold targets at depth in the Xiadian district that warrant focused exploration efforts.

Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

ABSTRACT Geographical information systems (GIS) are essential tools for mineral prospectivity modeling (MPM). Three-dimensional (3D) MPM is able to learn the association between geological evidence and mineralization in shallow zones and thereby build a prospectivity model for deep zones, making it a desirable technique to target deep-seated orebodies. However, existing 3D MPM methods directly generalize the model learned in shallow zones to the deep zones without attention to model transferability caused by the different metallogenic mechanisms between the two zones. In this study, we aim to robustly transfer the prospectivity model learned from shallow zones to deep zones. We cast the 3D MPM as a domain adaptation problem, which is an important realm of transfer learning. Because the metallogenic mechanism can be closely associated with spatial locations, we specifically focus on domain adaption concerning the spatial locations that are ignored by conventional domain adaptation methods. To measure the spatial-associated domain discrepancy, we propose a novel spatial-associated maximum mean discrepancy (SAMMD), which compares the joint distributions of features and spatial locations across domains. Based on the SAMMD criterion, a deep neural network, referred to as the spatial-associated domain adaptation network, is devised to learn cross-domain but mineralization-indicative features for building prospectivity model that is transferable to deep zones. A case study of the world-class Sanshandao gold deposit, in eastern China, was carried out to validate the effectiveness of the proposed methods. The results show that compared with other leading MPM methods and other domain adaption variants, the proposed method has superior prediction accuracy and targeting efficiency, demonstrating the effectiveness and robustness of the proposed method in targeting deep-seated orebodies in areas with different metallogenic mechanisms and no labeled data.

Genetic Algorithm Optimized Light Gradient Boosting Machine for 3D Mineral Prospectivity Modeling of Cu Polymetallic Skarn-Type Mineralization, Xuancheng Area, Anhui Province, Eastern China

Genetic Algorithm Optimized Light Gradient Boosting Machine for 3D Mineral Prospectivity Modeling of Cu Polymetallic Skarn-Type Mineralization, Xuancheng Area, Anhui Province, Eastern China

3D convolutional neural Network-based 3D mineral prospectivity modeling for targeting concealed mineralization within Chating area, middle-lower Yangtze River metallogenic Belt, China

The Chating area is situated within the Middle-Lower Yangtze River Metallogenic Belt, China. Several concealed skarn and porphyry-type deposits have been discovered in this area, indicating high potential for hosting hydrothermal deposits. However, due to the complex geological structure, exploration risks significantly increase with increasing depth. To overcome this challenge, three-dimensional mineral prospectivity modeling (3DMPM) has begun to be widely applied for mapping the prospectivity of deep-seated and concealed mineralization. However, most previous studies on 3DMPM were based on shallow supervised machine learning models and dimensionality-reduced 3D predictive maps. Although these models have shown good results, they may lose spatial correlation within the 3D predictive maps and fail to explore nonlinear correlations between the 3D predictive maps and mineralization. Meanwhile, 3D geological models are the most important basis of the 3DMPM, however, in the past, few studies have incorporated the optimization of the 3D geological models into the process of 3DMPM. Therefore, this paper initially builds and optimizes 3D geological models through implicit 3D geological modeling and “total litho-inversion” approach. Subsequently, the 3D predictive maps are generated by employing various 3D methods, which are further integrated using a 3D convolutional neural network (3D CNN) model to identify highly prospective areas for mineralization. The results show that the highly prospective areas identified by the 3DMPM include not only the training data but also other mineral deposits that have previously been discovered within the study area. In addition, compared with the Logistic Regression model (LR), Support Vector Machines (SVM), and Radom Forest (RF), the 3D CNN performs better prediction capabilities due to its enhanced ability to capture the correlations between 3D predictive maps and multiple types of mineral deposits. It suggests that the 3DMPM based on the 3D CNN model has commendable predictive capabilities in identifying prospective mineralization areas, and some new highly prospective areas can be considered as priority areas for future exploration of concealed mineralization within the Chating Area.

3D mineral exploration Cu-Zn targeting with multi-source geoscience datasets in the Weilasituo-bairendaba district, Inner Mongolia, China

The Weilasituo-bairendaba district is located at the eastern end of the Central Asian Orogenic Belt, which is an important component of the Cu-Pb-Zn polymetallic metallogenic belt on the Western slope of the Greater Xing’an Range in Inner Mongolia, China. The known Cu-Zn deposits such as the Weilasituo Cu-Zn deposit and the Bairendaba Ag-Pb-Zn deposit are the same tectonic-magmatic product. The district’s structure framework consists of the NE-trending regional faults, while the secondary faults provide channels and space for mineralization. The ore-bearing rocks are either Baoyintu Group gneisses or quartz diorites. The typical Cu-Zn deposits exhibit obvious Cu, Pb, Zn geochemical anomaly as well as obvious magnetic anomaly. The district-scale two-dimensional (2D) mineral prospectivity modeling has been reported. Nowadays, three-dimensional (3D) mineral prospectivity modeling is necessary and urgent. Integrated deposit geology and accumulated exploration data, the above four exploration criteria (regional fault, secondary fault, geochemical anomaly and magnetic susceptibility) are used for 3D mineral prospectivity modeling. Filtering (upward continuation, low pass filtering, two-dimensional empirical mode decomposition), magnetic inversion and 3D modeling techniques were used to construct geological models. Excellent machine learning algorithms such as random forest (RF) and XGBoost are applied. The two machine learning methods confirm each other to improve the accuracy of 3D mineral prospectivity modeling. In this paper, repeated random sampling and Bayesian optimization are combined to construct and tune models. This joint method can avoid the contingency caused by random sampling of negative samples, and can also realize automatic optimization of hyperparameters. The optimal models (RF28 and XGBoost11) were selected among thirty repeated training models for mineral prospectivity modeling. The obtained areas under the ROC curves of RF28 and XGBoost11 were 0.987 and 0.986, respectively. The prediction-area (P-A) plot and C-A fractal were used to delineate targets and grade targets. The targets were divided into Ⅰ-level targets and Ⅱ-level targets. The I- and II-targets are not only highly consistent with the known Cu-Zn deposits, but also exhibit obvious ore-forming geological features. The 3D targets are beneficial for Cu-Zn exploration in the Weilasituo-bairendaba district.

A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry–epithermal mineralization in the Duolong ore District, Tibet

Machine learning (ML) is emerging as a highly effective technique for mineral exploration. However, mineral exploration poses several unique challenges to ML application, such as uncertain geological information in remote regions and imbalanced labeled training data. In this study, we developed a deep-learning framework — a self-attention back-propagation neural network (SA-BPNN) — which is used to automatically explore relationships among diverse features and improve the capability of information extraction. Moreover, we proposed a mineral prospectivity modeling workflow involving “quantitative data + ML + expert experience” for porphyry-epithermal deposits. Using quantitative data obtained from hyperspectral remote sensing, geochemistry, and geophysics, we predicted ore-prospecting targets by applying the SVM, SA-BPNN, and U-Net models. Thereafter, we combined the model-based prediction with geological data to delineate the target areas. The model-based prediction by SVM, SA-BPNN, and U-Net occupy 1.73%, 1.40%, and 2.21% of the study area and contain 100%, 100%, and 80% of the known Cu-Au mineralization in the Duolong ore district in Tibet, respectively. The proposed SA-BPNN method, thus, achieved superior performance for mineral prospectivity modeling compared with alternative methods.

Best worst method-based mineral prospectivity modeling over the Central part of the Southern Kibi-Winneba Belt of Ghana

Best worst method-based mineral prospectivity modeling over the Central part of the Southern Kibi-Winneba Belt of Ghana

Mineral prospectivity mapping using attention-based convolutional neural network

Data-driven mineral prospectivity mapping (MPM) based on deep learning methods has become a powerful tool for mineral exploration targeting in the past years. Convolutional neural networks (CNNs) have shown great success in this field because of their powerful ability to capture the complex spatial geo-anomalies related to mineralization. However, the exploration big data applied to MPM mainly relies on the high dimensions of evidence layers (other than spatial dimensions), namely, a large number of channels. This impedes the extraction of key channel features related to mineralization when using traditional CNNs. In this paper, we developed an ensemble MPM method based on CNN and Attention model: the ATT–CNN method. Specifically, a channel attention layer is added after the convolution operation of the CNN to enhance the extraction of key channel features in complex exploration data, thereby improving the feature extraction ability and prediction accuracy of CNN for MPM. A case study of W–Sn mineral prospectivity modeling in the Nanling metallogenic belt in South China was used to verify the proposed method. To alleviate the issue of training sample scarcity, we used data augmentation methods (including sliding window and random zero noise addition) when training CNN models. The results show that the prediction accuracies of the ATT–CNN model (92.949% and 94.872% using sliding window and random zero noise addition, respectively) are higher than those of the traditional CNN (91.667% and 92.308%, respectively). Moreover, the improved areas under the receiver operating characteristic curves (AUC) of ATT–CNN (0.987 and 0.971) compared to those of the CNN (0.970 and 0.964) suggest that the proposed ensemble method improves the geological generalization of CNN. The high agreement with known deposits suggests that the areas targeted in this study can guide future mineral exploration of the W–Sn mineralization in the Nanling range.

Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.