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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| Published in: | Natural resources research (New York, N.Y.) Vol. 31; no. 3; pp. 1103 - 1119 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.06.2022
Springer Nature B.V |
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| ISSN: | 1520-7439, 1573-8981 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Yang, Jianhua Yang, Na Hong, Zenglin Zhang, Zhenkai |
| Author_xml | – sequence: 1 givenname: Na surname: Yang fullname: Yang, Na organization: School of Automation, Northwestern Polytechnical University, Research Center of Intelligent Geological Survey, Northwestern Polytechnical University – sequence: 2 givenname: Zhenkai surname: Zhang fullname: Zhang, Zhenkai organization: School of Automation, Northwestern Polytechnical University, Research Center of Intelligent Geological Survey, Northwestern Polytechnical University, Shaanxi Center of Mineral Geological Survey, Natural Resources Shaanxi Satellite Application Technology Center – sequence: 3 givenname: Jianhua surname: Yang fullname: Yang, Jianhua email: yangjianhua@nwpu.edu.cn organization: School of Automation, Northwestern Polytechnical University, Research Center of Intelligent Geological Survey, Northwestern Polytechnical University – sequence: 4 givenname: Zenglin surname: Hong fullname: Hong, Zenglin email: hongzenglin1963@163.com organization: School of Automation, Northwestern Polytechnical University, Research Center of Intelligent Geological Survey, Northwestern Polytechnical University, Shaanxi Institute of Geological Survey |
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| Cites_doi | 10.1016/j.measurement.2017.12.006 10.1016/j.patrec.2005.10.010 10.1007/s00521-019-04341-3 10.1007/s11053-019-09510-8 10.1016/j.apgeochem.2021.105043 10.1016/j.measurement.2021.109097 10.1016/j.apgeochem.2021.104940 10.1007/s12583-021-1440-0 10.1016/j.oregeorev.2015.12.005 10.1016/j.oregeorev.2015.01.001 10.1145/3065386 10.1016/j.jafrearsci.2016.11.032 10.1007/s11053-017-9357-0 10.1016/j.oregeorev.2019.04.003 10.1016/j.cageo.2010.09.014 10.1007/s11053-021-09934-1 10.1016/j.oregeorev.2017.11.001 10.1016/j.jafrearsci.2016.04.019 10.1016/j.cageo.2015.04.007 10.1016/j.oregeorev.2014.08.010 10.1007/s11053-010-9112-2 10.1016/j.cageo.2015.03.007 10.1016/j.gsf.2020.02.012 10.1038/s41586-019-1583-7 10.1029/2019MS001705 10.1016/j.apgeochem.2020.104747 10.1016/j.oregeorev.2018.10.006 10.1002/2018GL077004 10.1016/j.gexplo.2015.10.008 10.1080/08120099.2017.1328705 10.1016/j.cageo.2014.10.004 10.1016/j.apgeochem.2020.104710 10.1016/j.cageo.2017.10.005 10.1016/j.gexplo.2021.106904 10.1007/s11053-020-09700-9 10.1016/j.mineng.2019.10589 10.1007/s11053-019-09471-y 10.1016/j.oregeorev.2014.09.024 10.1016/j.measurement.2018.10.094 10.1016/j.cageo.2015.10.006 10.1016/j.gexplo.2015.06.018 10.1007/s11053-017-9345-4 10.1016/j.jestch.2020.01.002 10.1007/s11430-015-5178-3 10.1111/1755-6724.13688 10.1016/j.cageo.2019.05.009 10.1016/j.cageo.2019.01.016 10.1016/j.gecco.2020.e00971 10.1016/j.oregeorev.2021.104264 10.1016/j.gexplo.2021.106839 10.1144/geochem2016-024 10.1016/j.oregeorev.2021.104316 |
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| Keywords | Feature extraction Random forest Convolutional autoencoder network Mineral prospectivity prediction |
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| References | Yang, Zhang, Yang, Hong, Shi (CR48) 2021; 30 Goetz, Brenning, Petschko, Leopold (CR11) 2015; 81 Wang, Zhang, Yin, Luo, Li (CR36) 2021; 12 Carranza, Laborte (CR3) 2015; 74 Zuo, Peng, Li, Xiong (CR67) 2021; 46 Wu, Li, Yuan, Li, Zhang (CR42) 2022; 232 Hao, Guo, Gu, Hu (CR13) 2019; 143 Zhang, Zhang, Yuan (CR55) 2001 Zhao (CR59) 2015; 34 Gao, Zhang, Xiong, Zuo (CR9) 2016; 75 Luo, Zuo, Xiong, Wang (CR23) 2021; 131 Yousefi, Nykänen (CR52) 2016; 164 Zuo, Carranza (CR65) 2011; 37 Zhang, Zuo, Xiong (CR57) 2016; 59 Zhou, Li, Wang, Xiao, Li, Gao (CR62) 2017; 36 Zuo, Zhang, Zhang, Carranza, Wang (CR68) 2015; 71 CR6 Xiong, Zuo (CR44) 2018; 111 Wang, Zuo, Xiong (CR37) 2019; 29 Zuo (CR63) 2017; 26 CR46 Zuo (CR64) 2020; 29 Gui, Chen, Wang (CR12) 2018; 38 Shabankareh, Hezarkhani (CR34) 2017; 128 Sun, Chen, Zhong, Liu, Wang (CR35) 2019; 109 Xiong, Zuo (CR43) 2016; 86 Maitre, Bouchard, Bédard (CR24) 2019; 130 Chen, Wu (CR7) 2017; 64 Wang, Wang, Gao, Chen, Liu (CR38) 2007; 40 Wang, Zuo, Dong (CR40) 2019; 28 Jiang, Xu, Wei (CR14) 2018; 45 Liu, Rui (CR20) 2016; 22 Zhao, Chen, Zuo (CR58) 2015; 164 CR10 CR54 CR53 Chen, Guan, Xiong, Liang, Wang, Xu (CR5) 2019; 125 McKay, Harris (CR26) 2015; 2 Zhao, Chen (CR60) 2021; 32 Pati, Panda, Tripathy, Pradhan, Patnaik (CR30) 2020; 23 Miao, Dong, Hao, Wang, Cao (CR27) 2021; 176 Weyn, Durran, Caruana (CR41) 2019; 11 Zhang, Chen, Yang, Wu, Ge (CR56) 2021; 28 Luo, Xiong, Zuo (CR22) 2020; 122 Chang, Zhu, Zhang, Zhang, Hu (CR4) 2018; 34 Li, Chen, Xiang (CR18) 2019; 32 Masci, Meier, Cireşan, Schmidhuber (CR25) 2011; 2011 Carranza, Laborte (CR2) 2015; 71 Wang, Chen, Hu, Zhang, Liu, Huang (CR39) 2018; 34 Liu, Zhu, Zhou (CR21) 2018; 34 Li (CR17) 2008; 22 Yousefi, Carranza (CR50) 2015; 79 Zhou, Zhou, Cui, Wang, Wang, Ding (CR61) 2016; 49 Fawcett (CR8) 2006; 27 Krizhevsky, Sutskever, Hinton (CR15) 2017; 60 Rahimi, Abedi, Yousefi, Bahroudi, Elyasi (CR31) 2021; 128 Mignan, Broccardo (CR28) 2019 Ramil, López, Pozo-Antonio (CR32) 2017; 117 Oh, Lee (CR29) 2010; 19 Yin, Xiao, Li, Zou (CR49) 2010; 29 Xiong, Zuo, Carranza (CR45) 2018; 102 Almasi, Yousefi, Carranza (CR1) 2017; 91 Rodriguez-Galiano, Sanchez-Castillo, Chica-Olmo, Chica-Rivas (CR33) 2015; 71 Liu, Jiang, Han, Zhou (CR19) 2019; 134 Zuo, Xiong (CR66) 2017; 27 Li, Li, Yuan, Jowitt, Zhang, Zhou (CR16) 2020; 122 Yousefi, Carranza (CR51) 2017; 128 R Zuo (10038_CR68) 2015; 71 C Pati (10038_CR30) 2020; 23 Y Chen (10038_CR7) 2017; 64 J Li (10038_CR17) 2008; 22 R Zuo (10038_CR63) 2017; 26 Z Zhang (10038_CR56) 2021; 28 R Zuo (10038_CR67) 2021; 46 L Chen (10038_CR5) 2019; 125 Z Zhang (10038_CR57) 2016; 59 H Rahimi (10038_CR31) 2021; 128 V Rodriguez-Galiano (10038_CR33) 2015; 71 M Yousefi (10038_CR52) 2016; 164 Y Zhou (10038_CR62) 2017; 36 J Wang (10038_CR37) 2019; 29 S Zhou (10038_CR61) 2016; 49 T Sun (10038_CR35) 2019; 109 C Miao (10038_CR27) 2021; 176 P Zhao (10038_CR60) 2021; 32 Z Wang (10038_CR40) 2019; 28 H Oh (10038_CR29) 2010; 19 JN Goetz (10038_CR11) 2015; 81 C Liu (10038_CR19) 2019; 134 H Hao (10038_CR13) 2019; 143 Y Wang (10038_CR39) 2018; 34 S Li (10038_CR18) 2019; 32 10038_CR10 10038_CR54 10038_CR53 Z Luo (10038_CR23) 2021; 131 A Mignan (10038_CR28) 2019 10038_CR6 R Zuo (10038_CR66) 2017; 27 Z Gui (10038_CR12) 2018; 38 Y Gao (10038_CR9) 2016; 75 Y Liu (10038_CR21) 2018; 34 Z Luo (10038_CR22) 2020; 122 EJM Carranza (10038_CR3) 2015; 74 A Krizhevsky (10038_CR15) 2017; 60 J Maitre (10038_CR24) 2019; 130 M Shabankareh (10038_CR34) 2017; 128 J Masci (10038_CR25) 2011; 2011 G Zhang (10038_CR55) 2001 L Liu (10038_CR20) 2016; 22 GQ Jiang (10038_CR14) 2018; 45 R Wang (10038_CR38) 2007; 40 H Wang (10038_CR36) 2021; 12 10038_CR46 R Zuo (10038_CR64) 2020; 29 R Zuo (10038_CR65) 2011; 37 EJM Carranza (10038_CR2) 2015; 71 T Fawcett (10038_CR8) 2006; 27 B Wu (10038_CR42) 2022; 232 J Yin (10038_CR49) 2010; 29 M Yousefi (10038_CR51) 2017; 128 JA Weyn (10038_CR41) 2019; 11 A Almasi (10038_CR1) 2017; 91 G McKay (10038_CR26) 2015; 2 Y Xiong (10038_CR45) 2018; 102 L Chang (10038_CR4) 2018; 34 M Yousefi (10038_CR50) 2015; 79 H Li (10038_CR16) 2020; 122 J Zhao (10038_CR58) 2015; 164 Y Xiong (10038_CR43) 2016; 86 N Yang (10038_CR48) 2021; 30 AJ Ramil (10038_CR32) 2017; 117 P Zhao (10038_CR59) 2015; 34 Y Xiong (10038_CR44) 2018; 111 |
| References_xml | – volume: 117 start-page: 90 year: 2017 end-page: 95 ident: CR32 article-title: A computer vision system for identification of granite-forming minerals based on RGB data and artificial neural networks publication-title: Measurement doi: 10.1016/j.measurement.2017.12.006 – volume: 27 start-page: 861 issue: 8 year: 2006 end-page: 874 ident: CR8 article-title: An introduction to ROC analysis publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2005.10.010 – volume: 32 start-page: 2037 issue: 7 year: 2019 end-page: 2053 ident: CR18 article-title: Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data publication-title: Neural Computing & Applications doi: 10.1007/s00521-019-04341-3 – volume: 29 start-page: 189 issue: 1 year: 2019 end-page: 202 ident: CR37 article-title: Mapping mineral prospectivity via semi-supervised random forest publication-title: Natural Resources Research doi: 10.1007/s11053-019-09510-8 – volume: 131 start-page: 105043 year: 2021 ident: CR23 article-title: Detection of geochemical anomalies related to mineralization using the GANomaly network publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2021.105043 – volume: 176 year: 2021 ident: CR27 article-title: Magnetic anomaly detection based on fast convergence wavelet artificial neural network in the aeromagnetic field publication-title: Measurement doi: 10.1016/j.measurement.2021.109097 – volume: 128 year: 2021 ident: CR31 article-title: Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2021.104940 – volume: 32 start-page: 269 issue: 2 year: 2021 end-page: 275 ident: CR60 article-title: Digital geosciences and quantitative mineral exploration publication-title: Journal of Earth Science doi: 10.1007/s12583-021-1440-0 – volume: 75 start-page: 16 year: 2016 end-page: 28 ident: CR9 article-title: Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.12.005 – volume: 71 start-page: 804 year: 2015 end-page: 818 ident: CR33 article-title: Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.01.001 – ident: CR54 – volume: 60 start-page: 84 issue: 6 year: 2017 end-page: 90 ident: CR15 article-title: ImageNet classification with deep convolutional neural networks publication-title: Communications of the ACM doi: 10.1145/3065386 – volume: 128 start-page: 116 year: 2017 end-page: 126 ident: CR34 article-title: Application of support vector machines for copper potential mapping in Kerman region, Iran publication-title: Journal of African Earth Sciences doi: 10.1016/j.jafrearsci.2016.11.032 – volume: 27 start-page: 5 issue: 1 year: 2017 end-page: 13 ident: CR66 article-title: Big data analytics of identifying geochemical anomalies supported by machine learning methods publication-title: Natural Resources Research doi: 10.1007/s11053-017-9357-0 – volume: 109 start-page: 26 year: 2019 end-page: 49 ident: CR35 article-title: GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2019.04.003 – volume: 37 start-page: 1967 issue: 12 year: 2011 end-page: 1975 ident: CR65 article-title: Support vector machine: A tool for mapping mineral prospectivity publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2010.09.014 – volume: 30 start-page: 3905 issue: 6 year: 2021 end-page: 3923 ident: CR48 article-title: A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation publication-title: Natural Resources Research doi: 10.1007/s11053-021-09934-1 – volume: 91 start-page: 1066 year: 2017 end-page: 1080 ident: CR1 article-title: Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2017.11.001 – volume: 128 start-page: 47 year: 2017 end-page: 60 ident: CR51 article-title: Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values publication-title: Journal of African Earth Sciences doi: 10.1016/j.jafrearsci.2016.04.019 – volume: 81 start-page: 1 year: 2015 end-page: 11 ident: CR11 article-title: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.04.007 – volume: 71 start-page: 777 year: 2015 end-page: 787 ident: CR2 article-title: Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.08.010 – volume: 34 start-page: 3217 issue: 11 year: 2018 end-page: 3224 ident: CR21 article-title: Application of convolutional neural network in prospecting prediction of ore deposits: Taking the Zhaojikou Pb-Zn ore deposit in Anhui Province as a case publication-title: Acta Petrologica Sinica – volume: 19 start-page: 103 issue: 2 year: 2010 end-page: 124 ident: CR29 article-title: Application of artificial neural network for gold-silver deposits potential mapping: A case study of Korea publication-title: Natural Resources Research doi: 10.1007/s11053-010-9112-2 – volume: 22 start-page: 62 issue: 01 year: 2008 end-page: 64 ident: CR17 article-title: Analysis on ore-controlling factors and prospecting potential of the Baguamiao-type gold deposits in Fengtai area, Shannxi publication-title: Mineral Resources and Geology – ident: CR46 – volume: 79 start-page: 69 year: 2015 end-page: 81 ident: CR50 article-title: Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.03.007 – volume: 46 start-page: 350 issue: 1 year: 2021 end-page: 358 ident: CR67 article-title: Challenges of geological prospecting big data mining and integration using deep learning algorithms publication-title: Earth Science – volume: 34 start-page: 1255 issue: 7 year: 2015 end-page: 1259 ident: CR59 article-title: Digital mineral exploration and quantitative evaluation in the big data age publication-title: Geological Bulletin of China – volume: 12 start-page: 351 issue: 1 year: 2021 end-page: 364 ident: CR36 article-title: Landslide identification using machine learning publication-title: Geoscience Frontiers doi: 10.1016/j.gsf.2020.02.012 – year: 2019 ident: CR28 article-title: One neuron versus deep learning in aftershock prediction publication-title: Nature doi: 10.1038/s41586-019-1583-7 – volume: 22 start-page: 223 issue: 02 year: 2016 end-page: 231 ident: CR20 article-title: Present situation and development tendency of metallogenic prediction publication-title: Journal of Geomechanics – volume: 11 start-page: 2680 issue: 8 year: 2019 end-page: 2693 ident: CR41 article-title: Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height from historical weather data publication-title: Journal of Advances in Modeling Earth Systems doi: 10.1029/2019MS001705 – volume: 122 year: 2020 ident: CR16 article-title: Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian-Zhangbaling area, Anhui Province, China publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2020.104747 – volume: 102 start-page: 811 year: 2018 end-page: 817 ident: CR45 article-title: Mapping mineral prospectivity through big data analytics and a deep learning algorithm publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2018.10.006 – volume: 45 start-page: 3706 issue: 8 year: 2018 end-page: 3716 ident: CR14 article-title: A deep learning algorithm of neural network for the parameterization of typhoon-ocean feedback in typhoon forecast models publication-title: Geophysical Research Letters doi: 10.1002/2018GL077004 – volume: 2 start-page: 125 issue: 25 year: 2015 end-page: 143 ident: CR26 article-title: Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada publication-title: Natural Resources Research – volume: 164 start-page: 94 year: 2016 end-page: 106 ident: CR52 article-title: Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2015.10.008 – volume: 49 start-page: 234 issue: 01 year: 2016 end-page: 240 ident: CR61 article-title: Application of logistic regression methods in geochemical data analysis and mineral exploration: Example from Karamay region publication-title: Northwestern Geology – volume: 29 start-page: 1564 issue: 10 year: 2010 end-page: 1571 ident: CR49 article-title: Application of BP neural network in the classification of geo-chemical survey data publication-title: Geological Bulletin of China – volume: 28 start-page: 1 issue: 3 year: 2021 end-page: 15 ident: CR56 article-title: Machine learning for mineral prospectivity: A case study of iron-polymetallic mineral prospectivity in Southwestern Fujian publication-title: Earth Science Frontiers – volume: 64 start-page: 639 issue: 5 year: 2017 end-page: 651 ident: CR7 article-title: Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data publication-title: Australian Journal of Earth Sciences doi: 10.1080/08120099.2017.1328705 – volume: 74 start-page: 60 year: 2015 end-page: 70 ident: CR3 article-title: Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2014.10.004 – volume: 122 year: 2020 ident: CR22 article-title: Recognition of geochemical anomalies using a deep variational autoencoder network publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2020.104710 – ident: CR53 – volume: 111 start-page: 18 year: 2018 end-page: 25 ident: CR44 article-title: GIS-based rare events logistic regression for mineral prospectivity mapping publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2017.10.005 – ident: CR10 – volume: 232 year: 2022 ident: CR42 article-title: Transfer learning and Siamese neural network based identification of geochemical anomalies for mineral exploration: A case study from the Cu–Au deposit in the NW Junggar area of northern Xinjiang Province, China publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2021.106904 – ident: CR6 – volume: 29 start-page: 3415 issue: 6 year: 2020 end-page: 3424 ident: CR64 article-title: Geodata science-based mineral prospectivity mapping: A review publication-title: Natural Resources Research doi: 10.1007/s11053-020-09700-9 – volume: 143 year: 2019 ident: CR13 article-title: Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data publication-title: Minerals Engineering doi: 10.1016/j.mineng.2019.10589 – volume: 28 start-page: 1285 issue: 4 year: 2019 end-page: 1298 ident: CR40 article-title: Mapping geochemical anomalies through integrating random forest and metric learning methods publication-title: Natural Resources Research doi: 10.1007/s11053-019-09471-y – year: 2001 ident: CR55 publication-title: Qinling orogenic belt and continental dynamics – volume: 71 start-page: 502 year: 2015 end-page: 515 ident: CR68 article-title: Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.09.024 – volume: 38 start-page: 34 issue: 01 year: 2018 end-page: 40 ident: CR12 article-title: Classification and forecasting of geological anomaly based on GEP-logistic regression: A case study from geochemical data of eastern Tianshan publication-title: Journal of Guilin University of Technology – volume: 134 start-page: 634 year: 2019 end-page: 648 ident: CR19 article-title: Slope displacement prediction using sequential intelligent computing algorithms publication-title: Measurement doi: 10.1016/j.measurement.2018.10.094 – volume: 86 start-page: 75 year: 2016 end-page: 82 ident: CR43 article-title: Recognition of geochemical anomalies using a deep autoencoder network publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.10.006 – volume: 164 start-page: 54 year: 2015 end-page: 64 ident: CR58 article-title: Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2015.06.018 – volume: 34 start-page: 314 issue: 02 year: 2018 end-page: 318 ident: CR4 article-title: Spatial correlation analysis of mineral resources information publication-title: Acta Petrologica Sinica – volume: 26 start-page: 457 issue: 4 year: 2017 end-page: 464 ident: CR63 article-title: Machine learning of mineralization-related geochemical anomalies: A review of potential methods publication-title: Natural Resources Research doi: 10.1007/s11053-017-9345-4 – volume: 23 start-page: 973 issue: 5 year: 2020 end-page: 981 ident: CR30 article-title: A novel hybrid machine learning approach for change detection in remote sensing images publication-title: Engineering Science and Technology, an International Journal doi: 10.1016/j.jestch.2020.01.002 – volume: 59 start-page: 556 issue: 3 year: 2016 end-page: 572 ident: CR57 article-title: A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China publication-title: Science China Earth Sciences doi: 10.1007/s11430-015-5178-3 – volume: 36 start-page: 327 issue: 02 year: 2017 end-page: 331 ident: CR62 article-title: Research progress on big data and intelligent modelling of mineral deposits publication-title: Bulletin of Mineralogy, Petrology and Geochemistry – volume: 40 start-page: 77 issue: 2 year: 2007 end-page: 84 ident: CR38 article-title: The main metal deposits metallogenic series and exploration direction in Feng-Tai ore cluster region, Shaanxi Province publication-title: Northwestern Geology – volume: 34 start-page: 1959 issue: 7 year: 2018 end-page: 1976 ident: CR39 article-title: Tectonic controls on polymetallic mineralization in the Fengxian-Taibai ore cluster zone, Western Qinling, Shannxi Province publication-title: Acta Petrologica Sinica doi: 10.1111/1755-6724.13688 – volume: 130 start-page: 84 year: 2019 end-page: 93 ident: CR24 article-title: Mineral grains recognition using computer vision and machine learning publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2019.05.009 – volume: 2011 start-page: 52 year: 2011 end-page: 59 ident: CR25 article-title: Stacked convolutional auto-encoders for hierarchical feature extraction publication-title: Artificial Neural Networks and Machine Learning-ICANN – volume: 125 start-page: 43 year: 2019 end-page: 54 ident: CR5 article-title: A spatially constrained multi-autoencoder approach for multivariate geochemical anomaly recognition publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2019.01.016 – volume: 37 start-page: 1967 issue: 12 year: 2011 ident: 10038_CR65 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2010.09.014 – volume: 38 start-page: 34 issue: 01 year: 2018 ident: 10038_CR12 publication-title: Journal of Guilin University of Technology – volume: 74 start-page: 60 year: 2015 ident: 10038_CR3 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2014.10.004 – volume: 34 start-page: 3217 issue: 11 year: 2018 ident: 10038_CR21 publication-title: Acta Petrologica Sinica – volume: 34 start-page: 1255 issue: 7 year: 2015 ident: 10038_CR59 publication-title: Geological Bulletin of China – volume: 102 start-page: 811 year: 2018 ident: 10038_CR45 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2018.10.006 – volume: 164 start-page: 54 year: 2015 ident: 10038_CR58 publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2015.06.018 – volume: 23 start-page: 973 issue: 5 year: 2020 ident: 10038_CR30 publication-title: Engineering Science and Technology, an International Journal doi: 10.1016/j.jestch.2020.01.002 – volume: 29 start-page: 1564 issue: 10 year: 2010 ident: 10038_CR49 publication-title: Geological Bulletin of China – volume: 60 start-page: 84 issue: 6 year: 2017 ident: 10038_CR15 publication-title: Communications of the ACM doi: 10.1145/3065386 – volume: 81 start-page: 1 year: 2015 ident: 10038_CR11 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.04.007 – volume: 27 start-page: 5 issue: 1 year: 2017 ident: 10038_CR66 publication-title: Natural Resources Research doi: 10.1007/s11053-017-9357-0 – volume: 32 start-page: 2037 issue: 7 year: 2019 ident: 10038_CR18 publication-title: Neural Computing & Applications doi: 10.1007/s00521-019-04341-3 – volume: 22 start-page: 223 issue: 02 year: 2016 ident: 10038_CR20 publication-title: Journal of Geomechanics – volume: 34 start-page: 1959 issue: 7 year: 2018 ident: 10038_CR39 publication-title: Acta Petrologica Sinica doi: 10.1111/1755-6724.13688 – volume: 26 start-page: 457 issue: 4 year: 2017 ident: 10038_CR63 publication-title: Natural Resources Research doi: 10.1007/s11053-017-9345-4 – volume: 45 start-page: 3706 issue: 8 year: 2018 ident: 10038_CR14 publication-title: Geophysical Research Letters doi: 10.1002/2018GL077004 – ident: 10038_CR10 doi: 10.1016/j.gecco.2020.e00971 – volume: 22 start-page: 62 issue: 01 year: 2008 ident: 10038_CR17 publication-title: Mineral Resources and Geology – volume: 2 start-page: 125 issue: 25 year: 2015 ident: 10038_CR26 publication-title: Natural Resources Research – volume: 128 year: 2021 ident: 10038_CR31 publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2021.104940 – volume: 122 year: 2020 ident: 10038_CR16 publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2020.104747 – volume: 75 start-page: 16 year: 2016 ident: 10038_CR9 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.12.005 – volume: 19 start-page: 103 issue: 2 year: 2010 ident: 10038_CR29 publication-title: Natural Resources Research doi: 10.1007/s11053-010-9112-2 – ident: 10038_CR54 doi: 10.1016/j.oregeorev.2021.104264 – volume: 71 start-page: 502 year: 2015 ident: 10038_CR68 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.09.024 – volume: 64 start-page: 639 issue: 5 year: 2017 ident: 10038_CR7 publication-title: Australian Journal of Earth Sciences doi: 10.1080/08120099.2017.1328705 – volume: 34 start-page: 314 issue: 02 year: 2018 ident: 10038_CR4 publication-title: Acta Petrologica Sinica – volume: 111 start-page: 18 year: 2018 ident: 10038_CR44 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2017.10.005 – volume: 134 start-page: 634 year: 2019 ident: 10038_CR19 publication-title: Measurement doi: 10.1016/j.measurement.2018.10.094 – volume: 143 year: 2019 ident: 10038_CR13 publication-title: Minerals Engineering doi: 10.1016/j.mineng.2019.10589 – volume: 128 start-page: 116 year: 2017 ident: 10038_CR34 publication-title: Journal of African Earth Sciences doi: 10.1016/j.jafrearsci.2016.11.032 – volume: 12 start-page: 351 issue: 1 year: 2021 ident: 10038_CR36 publication-title: Geoscience Frontiers doi: 10.1016/j.gsf.2020.02.012 – volume: 40 start-page: 77 issue: 2 year: 2007 ident: 10038_CR38 publication-title: Northwestern Geology – volume: 29 start-page: 3415 issue: 6 year: 2020 ident: 10038_CR64 publication-title: Natural Resources Research doi: 10.1007/s11053-020-09700-9 – volume: 27 start-page: 861 issue: 8 year: 2006 ident: 10038_CR8 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2005.10.010 – ident: 10038_CR53 doi: 10.1016/j.gexplo.2021.106839 – ident: 10038_CR6 doi: 10.1144/geochem2016-024 – volume: 117 start-page: 90 year: 2017 ident: 10038_CR32 publication-title: Measurement doi: 10.1016/j.measurement.2017.12.006 – volume: 2011 start-page: 52 year: 2011 ident: 10038_CR25 publication-title: Artificial Neural Networks and Machine Learning-ICANN – volume: 176 year: 2021 ident: 10038_CR27 publication-title: Measurement doi: 10.1016/j.measurement.2021.109097 – volume: 46 start-page: 350 issue: 1 year: 2021 ident: 10038_CR67 publication-title: Earth Science – volume: 128 start-page: 47 year: 2017 ident: 10038_CR51 publication-title: Journal of African Earth Sciences doi: 10.1016/j.jafrearsci.2016.04.019 – volume: 109 start-page: 26 year: 2019 ident: 10038_CR35 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2019.04.003 – volume: 79 start-page: 69 year: 2015 ident: 10038_CR50 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.03.007 – volume: 11 start-page: 2680 issue: 8 year: 2019 ident: 10038_CR41 publication-title: Journal of Advances in Modeling Earth Systems doi: 10.1029/2019MS001705 – volume: 28 start-page: 1 issue: 3 year: 2021 ident: 10038_CR56 publication-title: Earth Science Frontiers – volume: 32 start-page: 269 issue: 2 year: 2021 ident: 10038_CR60 publication-title: Journal of Earth Science doi: 10.1007/s12583-021-1440-0 – volume: 125 start-page: 43 year: 2019 ident: 10038_CR5 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2019.01.016 – volume: 91 start-page: 1066 year: 2017 ident: 10038_CR1 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2017.11.001 – volume: 130 start-page: 84 year: 2019 ident: 10038_CR24 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2019.05.009 – volume: 232 year: 2022 ident: 10038_CR42 publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2021.106904 – volume: 30 start-page: 3905 issue: 6 year: 2021 ident: 10038_CR48 publication-title: Natural Resources Research doi: 10.1007/s11053-021-09934-1 – volume: 36 start-page: 327 issue: 02 year: 2017 ident: 10038_CR62 publication-title: Bulletin of Mineralogy, Petrology and Geochemistry – volume: 131 start-page: 105043 year: 2021 ident: 10038_CR23 publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2021.105043 – volume: 28 start-page: 1285 issue: 4 year: 2019 ident: 10038_CR40 publication-title: Natural Resources Research doi: 10.1007/s11053-019-09471-y – ident: 10038_CR46 doi: 10.1016/j.oregeorev.2021.104316 – volume: 122 year: 2020 ident: 10038_CR22 publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2020.104710 – volume: 71 start-page: 804 year: 2015 ident: 10038_CR33 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.01.001 – volume: 71 start-page: 777 year: 2015 ident: 10038_CR2 publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.08.010 – volume: 49 start-page: 234 issue: 01 year: 2016 ident: 10038_CR61 publication-title: Northwestern Geology – volume: 86 start-page: 75 year: 2016 ident: 10038_CR43 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2015.10.006 – volume: 29 start-page: 189 issue: 1 year: 2019 ident: 10038_CR37 publication-title: Natural Resources Research doi: 10.1007/s11053-019-09510-8 – volume: 59 start-page: 556 issue: 3 year: 2016 ident: 10038_CR57 publication-title: Science China Earth Sciences doi: 10.1007/s11430-015-5178-3 – year: 2019 ident: 10038_CR28 publication-title: Nature doi: 10.1038/s41586-019-1583-7 – volume-title: Qinling orogenic belt and continental dynamics year: 2001 ident: 10038_CR55 – volume: 164 start-page: 94 year: 2016 ident: 10038_CR52 publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2015.10.008 |
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| Title | Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest |
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