An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits

Roshanravan, Bijan; Aghajani, Hamid; Yousefi, Mahyar; Kreuzer, Oliver P.

doi:10.1007/s11053-018-9439-7

Cited by 24 publications

(5 citation statements)

References 80 publications

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“…The generally low correlation (<0.7) amongst all predictor variables suggests the absence of data redundancy and validates their effective integration for generating mineral predictive maps [91]. An augmented analysis using the prediction area plots was very effective in evaluating the prediction ability of different spatial data and served as a weighted value for optimising the knowledge driven models [64,65,92]. Within the Malumfashi area, a comparative assessment of three multi-criteria models (TOPSIS, ARAS and MOORA) were made and spatial evidence from these models suggest a high potential for gold mineralisation is more likely to occur in southern, central, and north-eastern part of the study area.…”

Section: Resultsmentioning

confidence: 99%

A spatial reconnaissance survey for gold exploration in a schist belt

Tende

Aminu

Amuda

et al. 2021

Heliyon

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Geological data integration and spatial analysis for structural elucidation are more assertive approaches for reconnaissance scale mineral exploration. In this study, several methods involving Fry analysis, distance correlation analysis, prediction area plots as well as knowledge driven predictive models including TOPSIS, ARAS and MOORA were systematically employed for unravelling the spatial geological attributes related to gold mineralisation. Additionally, statistical validation of knowledge driven predictive models were implemented using the Receiver Operating Characteristic/Area Under Curve analysis (ROC/AUC). The evidence from Fry and distance correlation analysis suggests that gold occurrence within parts of the Malumfashi schist belt of Nigeria is defined by a strong spatial association with the ENE-WSW as well as the NNE-SSW trending structures. The prediction area plot also revealed a robust spatial correlation between mineral occurrence and spatial data related to geological structures. The application of knowledge driven predictive models suggest a high favourability for gold occurrence within the southern, central, and north-eastern parts of the study location, while statistical validation using the ROC/AUC curves suggest a high prediction accuracy greater than 70% for all models. The geospatial analysis for mineral exploration within the Malumfashi area has unveiled an invaluable geological criterion for gold targeting with a considerable level of certainty.

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Section: Resultsmentioning

confidence: 99%

A spatial reconnaissance survey for gold exploration in a schist belt

Tende

Aminu

Amuda

et al. 2021

Heliyon

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“…The P-A plot analysis was accepted to determine suitable threshold that can evaluate prediction rate of the tungsten polymetallic deposits with smaller area including more deposits. Many researchers have evaluated the model performance and quality through prediction-area (P-A) plots (e.g., Nezhad et al, 2017;Zhang et al 2017a;Roshanravan et al, 2018;Liu et al, 2019). Yousefi et al (2012Yousefi et al ( , 2013 plotted and compared distribution against predicted percentage of known ore deposit.…”

Section: Prediction-area Plotmentioning

confidence: 99%

Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

Liu

Yonghang

2022

Preprint

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The current study aimed at assessing the capabilities of five machine learning models in term of mapping tungsten polymetallic prospectivity in the Gannan region of China. The five models include logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Geochemical, lithostratigraphic, and structural datasets were used to generate 16 evidential maps, which were integrated into the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performances of the models were evaluated through receiver operating characteristic (ROC) and K-fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb and Cd, implying that these are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict maximum known deposits within a minimum area, based on the prediction-area plot analysis of the five models, while the RF model can capture the most well-known deposits within the smallest study area. Finally, eighteen prospective areas were delineated according to the predicting results of the machine learning models, which will provide important guidance for further tungsten polymetallic exploration and associated studies.

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“…Then, confusion matrices were constructed and used to quantify the relative importance of the different prospectivity models. Thus, if two different prospectivity models delineated exploration targets in different occupied areas, but with the same prospectivity score, the performance of the prospectivity model with the smaller target areas is higher than that of the model with larger target areas [78]. Results of sensitivity analysis of prospectivity models are presented in Section 6.4.…”

Section: Prospectivity Mapping Processmentioning

confidence: 99%

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

2022

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This study aimed to model the prospectivity for placer deposits using geomorphic and landscape parameters. Within a geographic information system (GIS), spatial autocorrelation analysis of 3709 geochemical samples was used to identify prospective and non-prospective targets for columbite-tantalite (Nb-Ta) placer deposits of Hana-Lobo (H-L) Geological Complex (West- Central Côte d’Ivoire, West Africa). Based on mineralization system analysis, hydrologic, geomorphologic and landscape parameters were extracted at the locations of the identified targets. Supervised automatic classification approaches were applied, including Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to find a prospectivity model complex enough to capture the nature of the data. Metrics such as cross-validation accuracy (CVA), Receiver Operating Characteristic (ROC) curves, Area Under Curve (AUC) values and F-score values were used to evaluate the performance and robustness of output models. Results of applying machine-learning algorithms demonstrated that predictions provided by the final RF and KNN models were very close (κ = 0.56 and CVA = 0.69; κ = 0.54 and CVA = 0.68, respectively) and those provided by the SVM models were slightly lower with κ = 0.46 and CVA = 0.63. Independent validation results confirmed the slightly higher performance of both KNN and RF prospectivity models, compared to final SVM. Sensitivity analyses of both KNN and RF prospectivity models for medium and high-grade Nb-Ta deposits show a prediction rate of up to 90%.

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An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits

Cited by 24 publications

References 80 publications

A spatial reconnaissance survey for gold exploration in a schist belt

A spatial reconnaissance survey for gold exploration in a schist belt

Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

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