Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest

The convolutional neural networks used widely in mineral prospectivity prediction usually perform mixed feature extraction for multichannel inputs. This results in redundant features and impacts further improvement of predictive performance. To solve this limitation, this paper utilized convolutiona...

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Vydané v:Natural resources research (New York, N.Y.) Ročník 31; číslo 3; s. 1103 - 1119
Hlavní autori: Yang, Na, Zhang, Zhenkai, Yang, Jianhua, Hong, Zenglin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.06.2022
Springer Nature B.V
Predmet:
Artificial neural networks
Chemistry and Earth Sciences
Classifiers
Computer Science
Earth and Environmental Science
Earth Sciences
Feature extraction
Fossil Fuels (incl. Carbon Capture)
Geography
Gold
Machine learning
Mathematical Modeling and Industrial Mathematics
Mineral Resources
Neural networks
Original Paper
Performance enhancement
Performance prediction
Physics
Predictions
Statistics for Engineering
Support vector machines
Sustainable Development
Unsupervised learning
Feature extraction
Random forest
Convolutional autoencoder network
Mineral prospectivity prediction
ISSN:1520-7439, 1573-8981
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Shrnutí:The convolutional neural networks used widely in mineral prospectivity prediction usually perform mixed feature extraction for multichannel inputs. This results in redundant features and impacts further improvement of predictive performance. To solve this limitation, this paper utilized convolutional autoencoder networks to mine latent high-level features for each predictor variable in parallel to prevent the mixing of features and to enhance the feature mapping outcomes of each channel. Convolutional autoencoder networks, as unsupervised learning methods, can handle input images with high-dimensional features. They can train samples without differences such that the reconstructed outputs can restore inputs as accurately as possible to reduce the extraction of irrelevant feature information. Moreover, convolutional autoencoder networks focus on finding the fewest features for representing all inputs, and the extracted features express internal spatial relations at a high level. It is helpful to improve the performance of metallogenic prediction. Hence, this paper arranged these obtained features into one-dimensional vectors to establish the inputs of classifiers. Through modeling with four classifiers (logistic regression, support vector machine, artificial neural network, and random forest), we achieved different models for mineral prospectivity prediction. According to the comprehensive evaluations, the random forest model outperformed the other models. Taking the prediction of gold deposits in the Fengxian region of Southern Qinling in China as an example, the predictive capability of the proposed integrated method was shown to be effective and reliable. The predicted high-potential areas can provide significant guidance for gold deposit exploration in the study area.
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ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-022-10038-7