Mapping mineral prospectivity using an extreme learning machine regression

Yongliang, Chen; Wu, Wei

doi:10.1016/j.oregeorev.2016.06.033

Cited by 119 publications

(40 citation statements)

References 52 publications

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“…The true positive and negative grid points defined in this section ( Figure 3a) are hereafter used as the ground truth data to evaluate the performances of OCSVMs in subsequent sections. were then optimally converted into binary geochemical evidence layers by using the Youden index to evaluate the spatial relationship between the converted geochemical evidences and the known mineral deposits [13,14,[21][22][23]. Figure 3b-r shows the 17 binary evidence maps selected for mineral prospectivity mapping in this study.…”

Section: Geological and Geochemical Datamentioning

confidence: 99%

“…The geological and geochemical evidences, spatially associated with known mineral deposits, were selected and converted into binary evidence map layers and used as the input data of OCSVM models. Binary geological evidences were selected by using the Youden index to evaluate spatial relationships between the geological evidences and known mineral deposits [13,14,[21][22][23]. Continuous geochemical evidences were selected by statistically testing whether there exists significant spatial relationships between the geochemical evidences and the known mineral deposits [13,14,[21][22][23].…”

Section: Geological and Geochemical Datamentioning

confidence: 99%

“…Binary geological evidences were selected by using the Youden index to evaluate spatial relationships between the geological evidences and known mineral deposits [13,14,[21][22][23]. Continuous geochemical evidences were selected by statistically testing whether there exists significant spatial relationships between the geochemical evidences and the known mineral deposits [13,14,[21][22][23]. The continuous geochemical evidences selected for mineral prospectivity mapping were then optimally converted into binary geochemical evidence layers by using the Youden index to evaluate the spatial relationship between the converted geochemical evidences and the known mineral deposits [13,14,[21][22][23].…”

Section: Geological and Geochemical Datamentioning

confidence: 99%

“…In order to use the bat algorithm to automatically optimize the initialization parameters of OCSVM in mineral prospectivity mapping, a model combining OCSVM with the bat algorithm is proposed in this study. The area under the curve (AUC) of OCSVM is calculated based on the OCSVM modeling result to measure the overall performance of the OCSVM model in mineral prospectivity mapping [13,14,[21][22][23]. In the combined model, the AUC value of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of OCSVM parameters are used to specify the search space of the bat population, and the iterative search process of the bat algorithm is used to automatically determine the optimal parameters of OCSVM.…”

Section: Introductionmentioning

confidence: 99%

“…The bat-optimized OCSVM was used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with both the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM of which the optimal initialization parameters are determined by the trial and error method (i.e., trial and error-optimized OCSVM). The receiver operating characteristic (ROC) curves and AUC values were used to evaluate the performances of these OCSVMs [13,14,[21][22][23]. Based on the data modeling results, the optimal threshold for extracting mineral targets is determined by using the Youden index.…”

Section: Introductionmentioning

confidence: 99%

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A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Yongliang

Zhao

2019

Minerals

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One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping.

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Section: Geological and Geochemical Datamentioning

confidence: 99%

Section: Geological and Geochemical Datamentioning

confidence: 99%

Section: Geological and Geochemical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Yongliang

Zhao

2019

Minerals

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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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A Review of Artificial Intelligence Applications in Mining and Geological Engineering

Bui

Nguyen

2020

Lecture Notes in Civil Engineering

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Mapping mineral prospectivity using an extreme learning machine regression

Cited by 119 publications

References 52 publications

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

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

A Review of Artificial Intelligence Applications in Mining and Geological Engineering

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