Prospectivity Mapping for Tungsten Polymetallic Mineral Resources, Nanling Metallogenic Belt, South China: Use of Random Forest Algorithm from a Perspective of Data Imbalance

Li, Tongfei; Qin, Xia; Zhao, Mengyang; Gui, Zhou; Leng, Shuai

doi:10.1007/s11053-019-09564-8

Cited by 44 publications

(11 citation statements)

References 92 publications

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“…Hence, its 2D images can not correctly pose geological information and create challenges for training classification frameworks. Thirdly, the CNNs need a lot samples for training network, otherwise it is troublesome to ensure satisfactory classification performance and robustness in mineral exploration targeting [15]. It is noteworthy, application of the robust DL approaches as purely data-driven can not be eventuated to reasonable results [2,3,10,16].…”

Section: Introductionmentioning

confidence: 99%

Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering

Sabbaghi,

Tabatabaei,

fathianpour

2024

Preprint

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Deep learning networks have extensively been used to detect multi-element geochemical anomalies linked to different mineral deposits in recent decade. Efficient detection of multi-element geochemical anomalies is a significant issue that should be done through capable processing methods. The two-dimensional (2D) convolutional neural networks (CNN) are a powerful subset of machine learning algorithms which can present magnificent conclusions. Because those have the ability to extract high-level features of complex inputs. But, there are several shortcomings in applying 2D images as input of the CNNs for multi-element geochemical anomaly mapping. This challenge can be solved designing one-dimensional (1D) CNN to convolve geochemical data table yielding unbiased maps produced. Also, great feature extraction ability of the CNNs can increase defining geological constraints and training their frameworks with frequency domain data which is contained superlative information. In this regard, a novel geologically-constrained convolutional deep learning (GCDL) algorithm has been developed to classify multi-element geochemical data table. Accordingly, constructed GCDL with frequency domain training data of the Cr mineralization in the Robat Sefid district, NE Iran, could demonstrate impressive results. Based on success-rate curves, produced map of frequency domain geochemical data has plotted 86.22% Cr occurrences via 28% corresponding area while produced map of spatial domain geochemical data has plotted 79.91% Cr occurrences via same corresponding area.

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Section: Introductionmentioning

confidence: 99%

Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering

Sabbaghi,

Tabatabaei,

fathianpour

2024

Preprint

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“…The synthetic sample also introduces uncertainty. The study of Li Tongfei et al [46] showed that the change in the oversampling rate would affect the final result of MPM. In this study, we modify the original training set using the advanced method, Borderline-SMOTE, to balance positive and negative samples.…”

Section: Introductionmentioning

confidence: 99%

3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data

Peng,

Wang,

Wang

et al. 2023

Minerals

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Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas with mineral potential. These 3D geological models are the typical data source for 3D prospective modeling. Geological data sets from multiple sources are used to construct 3D geological models. Since in practice there is a significant imbalance in the ratio of mineralized to non-mineralized classes, the classification results will be biased in favor of the more observed classes. Borderline-SMOTE (BLSMOTE) is an oversampling technique used to solve the problem of unbalanced datasets and works by generating synthetic data points along the boundary line between the minority and majority classes. This helps to create a more balanced dataset without introducing too much noise. Non-mineralized samples can be generated by randomly selecting non-mineralized locations, which means that uncertainties are generated. In this paper, we take the shallow-forming low-temperature hydrothermal deposit Guizhou Lannigou gold deposit as an example to extract the ore-controlling elements and establish a 3D geological model. A total of 50 training samples are generated using the sampling method described above, and 50 mineralization prospects are generated using Random Forests. A return–risk analysis was used to explore the uncertainties associated with synthetic positive samples and randomly selected negative samples, and to determine the final mineral potential values. Based on the evaluation metrics G-mean and F-value, the model using BLSMOTE outperforms the model without the synthetic algorithm and the models using SMOTE and KMeansSMOTE. The optimal model BLSMOTE18 has an AUC of 0.9288. The methodology also performs superiorly with different levels of class imbalance datasets. Excluding the predictions where the results highly overlap with known deposits, five target zones were circled for the targets using a P-A plot, all of which have obvious metallogenic geological features. Among them, Target1 and Target2 have good potential for mineralization, which is of great significance for future mineral exploration work.

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“…Mineral prospectivity mapping (MPM) is concerned with quantifying and mapping the likelihood that mineral deposits are present at a certain location, which require the application of diverse methods and techniques to integrate multi-sources spatial geoscience datasets (Carranza 2009(Carranza , 2017. The application of machine learning methods for MPM improves efficiency and accuracy of mineral exploration and has become a major trend (Li et al, 2020;Wang et al, 2020a;Liu et al 2022). Machine learning is an important branch of artificial intelligence, and has a strong ability for mineral prediction.…”

Section: Introductionmentioning

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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Prospectivity Mapping for Tungsten Polymetallic Mineral Resources, Nanling Metallogenic Belt, South China: Use of Random Forest Algorithm from a Perspective of Data Imbalance

Cited by 44 publications

References 92 publications

Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering

Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering

3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data

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

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