Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning
Abstract
:1. Introduction
2. Study Area
3. Methods
3.1. Rock Samples from Drilling Cores and Magnetite Mine
3.2. Laboratory Experiment
3.3. Data Preprocessing
3.4. ML for Rock Classification
3.5. Optimizing the Hyperparameters of ML Methods
4. Results
4.1. Validation of ML Methods
4.2. Application to Geophysical Field Data
5. Discussion
5.1. Difference between Laboratory Experiment and Inversion Results
5.2. Accuracy of Inversion Results for Field Exploration
5.3. Training Materials from Drilling Cores
5.4. Verification of the Classfication Results from Field Data
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Property | Skewness | Kurtosis | ||
---|---|---|---|---|
Raw | Transformed | Raw | Transformed | |
Resistivity (Ωm) | 5.28 | −0.42 | 43.42 | −1.22 |
Chargeability (ms) | 0.61 | 0.01 | −0.12 | −1.40 |
Magnetic susceptibility | 1.84 | −0.26 | 2.37 | −1.37 |
Model | Optimal Hyperparameter Set |
---|---|
SVM | ‘C’ = 100, ‘gamma’ = 1, ‘kernel’ = rbf |
RF | ‘n_estimators’ = 100, ‘max_depth’ = 5, ‘max_features’ = 3, ‘min_samples_leaf’ = 3, ‘min_samples_split’=8, |
XGB | ‘n_estimators’ = 50, ‘learning_rate’ = 1, ‘max_depth’ = 7, ‘gamma’ = 1, ‘colsample_bytree’ = 0.6 |
LGBM | ‘n_estimators’ = 500, ‘colsample_bytree’ = 0.9, ‘max_depth’ = 5, ‘num_leaves’ = 30, ‘subsample’ = 0.2 |
DNN | ‘n_hidden_layer’ = 2, ‘n_hidden_nodes’ = (100, 100), ‘activation_function’ = (Elu, Elu), ‘optimizer’ = Adam, ‘drop_out_ratio’ = 0, ‘learning_rate’ = 0.01 |
Model | Accuracy | Recall-Score | Precision | F1-Score |
---|---|---|---|---|
SVM | 0.950 | 0.957 | 0.942 | 0.950 |
RF | 0.967 | 0.956 | 0.974 | 0.964 |
XGB | 0.958 | 0.971 | 0.948 | 0.958 |
LGBM | 0.967 | 0.956 | 0.974 | 0.964 |
DNN | 0.975 | 0.969 | 0.978 | 0.974 |
Mean | SD | Correlations | |||
---|---|---|---|---|---|
LOGRESI | SQRTCHAR | LOGSI | |||
Rock samples | |||||
LOGRESI | 2.75 | 1.43 | 1.00 | ||
SQRTCHAR | 12.99 | 7.58 | −0.78 | 1.00 | |
LOGSI | −1.74 | 1.21 | −0.85 | 0.85 | 1.00 |
Field data | |||||
LOGRESI | 4.02 | 0.61 | 1.00 | ||
SQRTCHAR | 2.00 | 2.25 | −0.36 | 1.00 | |
LOGSI | −1.40 | 0.63 | −0.41 | 0.61 | 1.00 |
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Shin, Y.; Shin, S. Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning. Minerals 2022, 12, 461. https://doi.org/10.3390/min12040461
Shin Y, Shin S. Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning. Minerals. 2022; 12(4):461. https://doi.org/10.3390/min12040461
Chicago/Turabian StyleShin, Youngjae, and Seungwook Shin. 2022. "Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning" Minerals 12, no. 4: 461. https://doi.org/10.3390/min12040461