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Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

  • Special Issue on Digital Geosciences and Quantitative Exploration of Mineral Resources
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Abstract

Today’s era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this performance degradation, deep learning models have been introduced in 3D MPM. In this study, taking the Huayuan sedimentary Mn deposit in Hunan Province as an example, we construct a 3D digital model of this deposit based on the prospectivity model of the study area. In this approach, 3D predictor layers are converted from the conceptual model and employed in a 3D convolutional neural network (3D CNN). The characteristics of the spatial distribution are extracted by the 3D CNN. Subsequently, we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3D CNN model and weight of evidence (WofE) method on each group. The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies, and the correlation between different ore-controlling factors. The analysis of 12 factors indicates that the 3D CNN model performs well in the 3D MPM, achieving a promising accuracy of up to 100% and a loss value below 0.001. A comparison shows that the 3D CNN model outperforms the WofE model in terms of predictive evaluation indexes, namely the success rate and ore-controlling rate. In particular, the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors. Consequently, we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults. The experimental results confirm that the proposed 3D CNN is promising for 3D MPM as it eliminates the interference factors.

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References Cited

  • Abedi, M., Norouzi, G. H., Bahroudi, A., 2012. Support Vector Machine for Multi-Classification of Mineral Prospectivity Areas. Computers & Geosciences, 46: 272–283. https://doi.org/10.1016/j.cageo.2011.12.014

    Article  Google Scholar 

  • Albora, A. M., Ucan, O. N., Ozmen, A., et al., 2001. Separation of Bouguer Anomaly Map Using Cellular Neural Network. Journal of Applied Geophysics, 46(2): 129–142. https://doi.org/10.1016/s0926-9851(01)00033-7

    Article  Google Scholar 

  • Bengio, Y., Lamblin, P., Popovici, D., et al., 2007. Greedy Layer-Wise Training of Deep Networks. In: Schölkopf, B., Platt, J., Hofmann, T., eds., Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. The MIT Press, Cambridge. 153–160

    Google Scholar 

  • Bilgili, E., Nucan, O., Albora, A. M., et al., 2002. Potential Anomaly Separation Using Genetically Trained Multi-Level Cellular Neural Networks, In: Proc. 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, Frankfurt. 391–398

  • Bristol, R. S., Euliss Jr, N. H., Booth, N. L., et al., 2012. Science Strategy for Core Science Systems in the U.S. Geological Survey, 2013–2023: Public Review Release, U. S. Geological Survey, Reston. 29

  • Brown, K., Dormer, J., Fei, B., et al., 2019. March. Deep 3D Convolutional Neural Networks for Fast Super-Resolution Ultrasound Imaging, In Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, San Diego, CA, 1095502

  • Brown, W. M., Gedeon, T. D., Groves, D. I., et al., 2000. Artificial Neural Networks: A New Method for Mineral Prospectivity Mapping. Australian Journal of Earth Sciences, 47(4): 757–770. https://doi.org/10.1046/j.1440-0952.2000.00807.x

    Article  Google Scholar 

  • Cai, H. H., Zhu, W., Li, Z. X., et al., 2019. Prediction Method of Tungsten-Molybdenum Prospecting Target Area Based on Deep Learning. Journal of Geo-information Science, 21: 928–936 (in Chinese with English Abstract)

    Google Scholar 

  • Chang, C. Y., Chen, S. J., Tsai, M. F., 2010. Application of Support-Vector-Machine-Based Method for Feature Selection and Classification of Thyroid Nodules in Ultrasound Images. Pattern Recognition, 43(10): 3494–3506. https://doi.org/10.1016/j.patcog.2010.04.023

    Article  Google Scholar 

  • Chen, H., Qi, X., Yu, L., et al., 2016. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation, In Proc. 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas. 2487–2496

  • Chen, J. P., 2008. The Research and Application of Neural Network Pattern Recognition Technique for Oil and Gas Geochemistry Base on MATLAB: [Dissertation]. China University of Geosciences, Beijing (in Chinese with English Abstract)

    Google Scholar 

  • Chen, J. P., Chen, Y., Zeng, M., et al., 2008. 3D Positioning and Quantitative Prediction of the Koktokay No. 3 Pegmatite Dike, Xinjiang, China, Based on the Digital Mineral Deposit Model. Geological Bulletin of China, 27: 552–559 (in Chinese with English Abstract)

    Google Scholar 

  • Chen, J. P., Lü, P., Wu, W., et al., 2007. A 3-D Prediction Method for Blind Orebody Based on 3-D Visualization Model and Its Application. Earth Science Frontiers, 14(5): 54–61. https://doi.org/10.1016/s1872-5791(07)60035-9

    Article  Google Scholar 

  • Chen, J. P., Shang, B. C., Lü, P., et al., 2009. Large-Scale 3D Metallogenic Prediction of Concealed Orebody in Gejiu, Yunnan Province. Chinese Journal of Geology, 44: 324–337 (in Chinese with English Abstract)

    Google Scholar 

  • Chen, J. P., Shi, R., Wang, L. M., et al., 2012a. 3D metallogenic Prediction for Western Section of Q8 Gold Deposit in Tongguan County of Shaanxi Based on Digital Mineral Deposit Model. Journal of Geology, 36: 237–242 (in Chinese with English Abstract)

    Google Scholar 

  • Chen, J. P., Wang, C. N., Shang, B. C., et al., 2012b. Three-Dimensional Metallogenic Prediction in Yongmei Region Based on Digital Ore Deposit Model. Scientific and Technological Management of Land and Resources, 29: 14–20 (in Chinese with English Abstract)

    Google Scholar 

  • Chen, M., Mao, S. W., Liu, Y. H., 2014. Big Data: A Survey. Mobile Networks and Applications, 19(2): 171–209. https://doi.org/10.1007/s11036-013-0489-0

    Article  Google Scholar 

  • Chen, S. M., 2012. Research of Multiple Geoscience Information Prospecting Prediction in Xikuangshan Antimony Ore Field: [Dissertation]. China University of Geosciences, Beijing. 306 (in Chinese with English Abstract)

    Google Scholar 

  • Chen, Y. L., Wu, W., 2017. Mapping Mineral Prospectivity Using an Extreme Learning Machine Regression. Ore Geology Reviews, 80: 200–213. https://doi.org/10.1016/j.oregeorev.2016.06.033

    Article  Google Scholar 

  • Chen, Y. L., Zhou, B., Li, X. B., 2012c. Mineral Target Prediction Based on Boltzmann Machines. Progress in Geophysics, 27: 179–185 (in Chinese with English Abstract)

    Google Scholar 

  • Ciresan, D., Giusti, A., Gambardella, L. M., et al., 2012. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images, In: Proc. 25th International Conference on Neural Information Processing Systems, Lake Tahoe. 2843–2851

  • Deng, L., 2019. Protein Structure Evaluation Based on 3DCNN: [Dissertation]. Jilin University, Changchun. 62 (in Chinese with English Abstract)

    Google Scholar 

  • Derevyanko, G., Grudinin, S., Bengio, Y., et al., 2018. Deep Convolutional Networks for Quality Assessment of Protein Folds. Bioinformatics, 34(23): 4046–4053. https://doi.org/10.1093/bioinformatics/bty494

    Article  Google Scholar 

  • Dizaji, M. S., Harris, D. K., 2019. 3D InspectionNet: A Deep 3D Convolutional Neural Networks Based Approach for 3D Defect Detection on Concrete Columns, In Proc. SPIE 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, Denver. 109710E

  • Du, Y. S., Zhou, Q., Yu, W. C., et al., 2015. Linking the Cryogenian Manganese Metallogenic Process in the Southeast Margin of Yangtze Block to Break-Up of Rodinia Supercontinent and Sturtian Glaciation. Geological Science and Technology Information, 34: 1–7 (in Chinese with English Abstract)

    Google Scholar 

  • Gonbadi, A. M., Tabatabaei, S. H., Carranza, E. J. M., 2015. Supervised Geochemical Anomaly Detection by Pattern Recognition. Journal of Geochemical Exploration, 157: 81–91. https://doi.org/10.1016/j.gexplo.2015.06.001

    Article  Google Scholar 

  • Hariharan, S., Tirodkar, S., Porwal, A., et al., 2017. Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia. Natural Resources Research, 26(4): 489–507. https://doi.org/10.1007/s11053-017-9335-6

    Article  Google Scholar 

  • Hinton, G., 2011. Deep Belief Nets. In: Sammut, C., Webb, G. I., eds., Encyclopedia of Machine Learning. Springer, Boston. 267–269

    Google Scholar 

  • Holtzman, B. K., Paté, A., Paisley, J., et al., 2018. Machine Learning Reveals Cyclic Changes in Seismic Source Spectra in Geysers Geothermal Field. Science Advances, 4(5): eaao2929. https://doi.org/10.1126/sciadv.aao2929

    Article  Google Scholar 

  • Houlding, B. S., Renholme, S., 1998. The Use of Solid Modeling in the Underground Mine Design. Computer Application in the Mineral Industry, 12: 67–89

    Google Scholar 

  • Jiang, S. Y., Chen, Y. M., 2017. Hand Gesture Recognition by Using 3DCNN and LSTM with Adam Optimizer, In Proc. 18th Pacific-Rim Conference on Multimedia, Harbin. 743–753

  • Kamnitsas, K., Ledig, C., Newcombe, V. F. J., et al., 2017. Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation. Medical Image Analysis, 36: 61–78. https://doi.org/10.1016/j.media.2016.10.004

    Article  Google Scholar 

  • Karasawa, H., Liu, C. L., Ohwada, H., 2018. Deep 3D Convolutional Neural Network Architectures for Alzheimer’s Disease Diagnosis, In Proc. 10th Asian Conference on Intelligent Information and Database Systems, Dong Hoi City. 287–296

  • Kirkwood, C., Cave, M., Beamish, D., et al., 2016. A Machine Learning Approach to Geochemical Mapping. Journal of Geochemical Exploration, 167: 49–61. https://doi.org/10.1016/j.gexplo.2016.05.003

    Article  Google Scholar 

  • Lawrence, S., Giles, C. L., Tsoi, A. C., et al., 1997. Face Recognition: A Convolutional Neural-Network Approach. IEEE Transactions on Neural Networks, 8(1): 98–113. https://doi.org/10.1109/72.554195

    Article  Google Scholar 

  • Le Cun, Y., Bottou, L., Bengio, Y., et al., 1998. Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11): 2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  • Leite, E. P., de Souza Filho, C. R., 2009. Artificial Neural Networks Applied to Mineral Potential Mapping for Copper-Gold Mineralizations in the Carajás Mineral Province, Brazil. Geophysical Prospecting, 57(6): 1049–1065. https://doi.org/10.1111/j.1365-2478.2008.00779.x

    Article  Google Scholar 

  • Li, C., Jiang, Y. L., Hu, M. K., et al., 2015. Study and Application of Gravity Anomaly Separation by Cellular Neural Networks. Computing Techniques for Geophysical and Geochemical Exploration, 37: 16–21 (in Chinese with English Abstract)

    Google Scholar 

  • Li, D., 2014. Comparison Research on Metallogenic Prediction Models Based on BP Neural Network and SVM—Taking an Example of Hatu-Baobei Gold Mines: [Dissertation]. University of Chinese Academy of Sciences, Beijing. 69 (in Chinese)

    Google Scholar 

  • Li, S., Chen, J. P., Xiang, J., 2019. Applications of Deep Convolutional Neural Networks in Prospecting Prediction Based on Two-Dimensional Geological Big Data. Neural Computing and Applications, 32(7): 2037–2053. https://doi.org/10.1007/s00521-019-04341-3

    Article  Google Scholar 

  • Li, T., Zuo, R. G., Xiong, Y. H., et al., 2021. Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Natural Resources Research, 30: 27–38. https://doi.org/10.1007/s11053-020-09742-z

    Article  Google Scholar 

  • Li, Y. L., Yan, Y. Q., Wu, Y. F., et al., 2020. Supermarket Security System Based on 3D CNN (Patent No. 109544837A), http://www.soopat.com/Patent/201811328968, 2019 (Accessed 8 May 2020) (in Chinese)

  • Li, Z. J., 1991. An Approach to the Large-Scale and Three-Dimensional Stereo Statistical Prediction of Mineral Deposits in Yueshan Region, Anhui Province. Earth Science, 16: 311–317 (in Chinese with English Abstract)

    Google Scholar 

  • Liao, J. G., Wang, S. L., Zhang, X. X., et al., 2018. 3D Convolutional Neural Networks Based Speaker Identification and Authentication, In: Proc. 25th IEEE International Conference on Image Processing, Athens. 2042–2046

  • Lin, N., 2015. Study on the Metallogenic Prediction Models Based on Remote Sensing Geology and Geochemical Information: A Case Study of Lalingzaohuo Region in Qinghai Province: [Dissertation]. Jilin University, Changchun. 107 (in Chinese with English Abstract)

    Google Scholar 

  • Liu, J. S., Zheng, Y. C., 1986. Discussion on the Genesis of Minle Manganese Deposits. Hunan Geology, 5: 18–26 (in Chinese with English Abstract)

    Google Scholar 

  • Liu, Y., Zhou, K. F., Xia, Q. L., 2017. A MaxEnt Model for Mineral Prospectivity Mapping. Natural Resources Research, 27(3): 299–313. https://doi.org/10.1007/s11053-017-9355-2

    Article  Google Scholar 

  • Liu, Y., Zhou, K. F., Zhang, N. N., et al., 2018. Maximum Entropy Modeling for Orogenic Gold Prospectivity Mapping in the Tangbale-Hatu Belt, Western Junggar, China. Ore Geology Reviews, 100: 133–147. https://doi.org/10.1016/j.oregeorev.2017.04.029

    Article  Google Scholar 

  • Liu, Z., Liu, M. C., Wei, W., et al., 2010. Gravity Anomaly Separation Based on Cellular Neural Network. Journal of China University of Petroleum (Edition of Natural Science), 34: 57–61 (in Chinese with English Abstract)

    Google Scholar 

  • Lohr, S., 2012. The Age of Big Data. The New York Times, 2012, February 11. https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html (accessed 10 May 2020)

  • Luo, J., Li, Y., 2019. Brain fMRI Signal Recognition Method Based on three-Dimensional Convolutional Neural Network. Chinese Journal of Stereology and Image Analysis, 24: 191–198 (in Chinese with English Abstract)

    Google Scholar 

  • Ma, Z. W., 2018. Research of Gesture Recognition Based on Densely Connected 3DCNN and Convolutional GRU: [Dissertation]. Fujian Normal University, Fuzhou. 67 (in Chinese)

    Google Scholar 

  • Mao, X. C., Dai, T. G., Wu, X. B., et al., 2009. The Stereoscopic Quantitative Prediction of Concealed Ore Bodies in the Deep and Marginal Parts of Crisis Mines: A Case Study of the Dachang Tin Polymetallic Ore Deposit in Guangxi. Geology in China, 36: 424–435 (in Chinese with English Abstract)

    Google Scholar 

  • Mao, X. C., Zou, Y. H., Chen, J., et al., 2010. Three-Dimensional Visual Prediction of Concealed Ore Bodies in the Deep and Marginal Parts of Crisis Mines: A Case Study of the Fenghnangshan Ore Field in Tongling, Annul, China. Geological Bulletin of China, 29: 401–413 (in Chinese with English Abstract)

    Google Scholar 

  • Martens, J., Sutskever, I., 2011. Learning Recurrent Neural Networks with Hessian-Free Optimization, In: Proc. 28th International Conference on Machine Learning, Bellevue. 1033–1040

  • Nie, H., Zhu, Y. Q., Chang, L. H., et al., 2018. Research on Construction Method of Data-Driven Minerals Prediction Model. China Mining Magazine, 27: 82–87 (in Chinese with English Abstract)

    Google Scholar 

  • O’Brien, J. J., Spry, P. G., Nettleton, D., et al., 2015. Using Random Forests to Distinguish Gahnite Compositions as an Exploration Guide to Broken Hill-Type Pb-Zn-Ag Deposits in the Broken Hill Domain, Australia. Journal of Geochemical Exploration, 149: 74–86. https://doi.org/10.1016/j.gexplo.2014.11.010

    Article  Google Scholar 

  • Oh, K., Kim, W., Shen, G. F., et al., 2019. Classification of Schizophrenia and Normal Controls Using 3D Convolutional Neural Network and Outcome Visualization. Schizophrenia Research, 212: 186–195. https://doi.org/10.1016/j.schres.2019.07.034

    Article  Google Scholar 

  • Phillips, S. J., Dudík, M., 2008. Modeling of Species Distributions with Maxent: New Extensions and a Comprehensive Evaluation. Ecography, 31(2): 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

    Article  Google Scholar 

  • Qi, H. F., Li, J., Wu, Q., et al., 2018. A 3D-CNN Based Video Hashing Method, In: Proc. SPIE 10806, Tenth International Conference on Digital Image Processing, Shanghai. 1080644

  • Rodriguez-Galiano, V. F., Chica-Olmo, M., Chica-Rivas, M., 2014. Predictive Modelling of Gold Potential with the Integration of Multisource Information Based on Random Forest: A Case Study on the Rodalquilar Area, Southern Spain. International Journal of Geographical Information Science, 28(7): 1336–1354. https://doi.org/10.1080/13658816.2014.885527

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., et al., 2015. Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines. Ore Geology Reviews, 71: 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001

    Article  Google Scholar 

  • Rong, J. H., Chen, J. P., Shang, B. C., 2012. Three-Dimensional Prediction of Blind Orebodies in Gejiu, Yunnan Province Based on the Ore-Search Model. Geology and Exploration, 48: 191–198 (in Chinese with English Abstract)

    Google Scholar 

  • Rouet-Leduc, B., Hulbert, C., Lubbers, N., et al., 2017. Machine Learning Predicts Laboratory Earthquakes. Geophysical Research Letters, 44(18): 9276–9282. https://doi.org/10.1002/2017gl074677

    Article  Google Scholar 

  • Sainath, T. N., Kingsbury, B., Ramabhadran, B., 2012. Auto-Encoder Bottleneck Features Using Deep Belief Networks, In: Proc. 38th IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto. 4153–4156

  • Sato, R., Ishida, T., 2019. Protein Model Accuracy Estimation Based on Local Structure Quality Assessment Using 3D Convolutional Neural Network. PLoS One, 14(9): e0221347. https://doi.org/10.1371/journal.pone.0221347

    Article  Google Scholar 

  • Simonyan, K., Zisserman, A., 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/abs/1409.1556 (accessed 11 May 2020)

  • Sun, S. T., He, Y. X., 2017. Multi-Label Emotion Classification for Microblog Based on CNN Feature Space. Advanced Engineering Sciences, 49: 162–169 (in Chinese with English Abstract)

    Google Scholar 

  • Tang, S. Y., 1990. Isotope Geological Study of Manganese Deposit in Minle Area, Hunan Province. Acta Sedimentologica Sinica, 8: 77–84 (in Chinese with English Abstract)

    Google Scholar 

  • Thorleifson, L. H., Berg, R. C., Russell, H. A. J., 2010. Geological Mapping Goes 3-D in Response to Societal Needs. GSA Today, 20: 27–29. https://doi.org/10.1130/gsatg86gw.1

    Article  Google Scholar 

  • Twarakavi, N. K. C., Misra, D., Bandopadhyay, S., 2006. Prediction of Arsenic in Bedrock Derived Stream Sediments at a Gold Mine Site under Conditions of Sparse Data. Natural Resources Research, 15(1): 15–26. https://doi.org/10.1007/s11053-006-9013-6

    Article  Google Scholar 

  • Wang, F., Bi, J. G., Wan, Z. C., et al., 2019. Transformer Fault Diagnosis Method Based on Deeply Convolutional Neural Network. Guangdong Electric Power, 32: 177–183 (in Chinese with English Abstract)

    Google Scholar 

  • Wang, H. Y., Li, X. F., Li, Y. B., et al., 2020. Non-Destructive Detection of apple Multi-Quality Parameters Based on Hyperspectral Imaging Technology and 3D-CNN. Journal of Nanjing Agricultural University, 43: 178–185 (in Chinese with English Abstract)

    Google Scholar 

  • Wang, J., Pan, G. T., 2009. Neoproterozoic South China Palaeocontinents: An Overview. Acta Sedimentologica Sinica, 27: 818–825 (in Chinese with English Abstract)

    Google Scholar 

  • Wang, X., Xie, W. X., Song, J. Y., 2018. Learning Spatiotemporal Features with 3DCNN and ConvGRU for Video Anomaly Detection, In: Proc. 14th IEEE International Conference on Signal Processing, Beijing. 474–479

  • Wu, J., Min, Y., Li, C., et al., 2019. A micro-Expression Recognition Algorithm Based on 3D-CNN. Telecommunication Engineering, 59: 1115–1120 (in Chinese with English Abstract)

    Google Scholar 

  • Wu, J. S., Huang, H., Yang, B., et al., 2001. Three Dimensional Orebody Simulation and Its Mineral Resource Assessment of Ashele Copper-Zinc Deposit in Xingjiang. Mineral Resources and Geology, 15: 119–123 (in Chinese with English Abstract)

    Google Scholar 

  • Wu, Z., 2019b. Reserach on 3D Face Recognition Based on Convolutional Neural Network: [Dissertation]. University of Science and Technology of China, Hefei (in Chinese with English Abstract)

    Google Scholar 

  • Xiang, J., Chen, J. P., Hu, B., et al., 2016a. 3D Metallogenic Prediction Based on 3D Geological-Geophysical Model: A Case Study in Tongling Mineral District of Anhui. Advances in Earth Science, 31: 603–614 (in Chinese with English Abstract)

    Google Scholar 

  • Xiang, J., Chen, J. P., Hu, Q., et al., 2016b. 3D Metallogenic Prediction Based on Minerogenetic Series: A Case Study in Tongling Mineral District of Anhui. Geoscience, 30: 230–238 (in Chinese with English Abstract)

    Google Scholar 

  • Xiao, K. Y., Li, N., Sun, L., et al., 2012. Largc Scale 3D Mineral Prediction Methods and Channels Based on 3D Information Technology. Journal of Geology, 36: 229–236 (in Chinese with English Abstract)

    Google Scholar 

  • Xiao, K. Y., Li, N., Wang, K., et al., 2015. Mineral Resources Assessment under the Thought of Big Data. Geological Bulletin of China, 34: 1266–1272 (in Chinese with English Abstract)

    Google Scholar 

  • Xing, W. W., Li, Y., Zhang, S. L., 2018. View-Invariant Gait Recognition Method by Three-Dimensional Convolutional Neural Network. Journal of Electronic Imaging, 27(1): 013010. https://doi.org/10.1117/1.jei.27.1.013010

    Article  Google Scholar 

  • Xiong, Y. H., Zuo, R. G., 2016. Recognition of Geochemical Anomalies Using a Deep Autoencoder Network. Computers & Geosciences, 86: 75–82. https://doi.org/10.1016/j.cageo.2015.10.006

    Article  Google Scholar 

  • Xiong, Y. H., Zuo, R. G., Carranza, E. J. M., 2018. Mapping Mineral Prospectivity through Big Data Analytics and a Deep Learning Algorithm. Ore Geology Reviews, 102: 811–817. https://doi.org/10.1016/j.oregeorev.2018.10.006

    Article  Google Scholar 

  • Xu, S. S., Yan, C., Gao, L. M., 2019. Lake Extraction Algorithm Based on Three-Dimensional Convolutional Neural Network. Journal of Computer Applications, 39: 3450–3455 (in Chinese with English Abstract)

    Google Scholar 

  • Xu, X. S., Huang, H. Q., Liu, B. J., et al., 1991. The Sedimentollgy and Origin of Early Sinian Manganese Deposits form the Datangpo Formation, South China. Acta Sedimentologica Sinica, 9: 63–72 (in Chinese with English Abstract)

    Google Scholar 

  • Yan, G. S., Xue, Q. W., Xiao, K. Y., et al., 2015. An Analysis of Major Problems in Geological Survey Big Data. Geological Bulletin of China, 34: 1273–1279 (in Chinese with English Abstract)

    Google Scholar 

  • Yan, Q., Chen, J. P., Shang, B. C., 2012. The 3D Prediction Model and Division of Targets in Lutangba Study Area of Gaosong Ore Field in Gejiu, Yunnan Province. Geoscience, 26: 286–293 (in Chinese with English Abstract)

    Google Scholar 

  • Yang, H., 2016. Research and Application of the Combination of Deep Learning and Principal Component Analysis: [Dissertation]. Chengdu University of Technology, Chengdu. 46 (in Chinese)

    Google Scholar 

  • Yang, R. D., Ouyang, Z. Y., Zhu, L. J., et al., 2002. A New Understanding of Manganese Carbonate Deposits in Early Sinian Datangpo Stage. Acta Mineralogica Sinica, 22: 329–334 (in Chinese with English Abstract)

    Google Scholar 

  • Yang, S. X., Lao, K. T., 2006. Mineralization Model for the Manganese Deposits in Northwestern Hunan: An Example from Minle Manganese Deposit in Huayuan, Hunan. Sedimentary Geology and Tethyan Geology, 26: 72–80 (in Chinese with English Abstract)

    Google Scholar 

  • Yu, P. P., Chen, J. P., Chai, F. S., et al., 2015. Research on Model-Driven Quantitative Prediction and Evaluation of Mineral Resources Based on Geological Big Data Concept. Geological Bulletin of China, 34: 1333–1343 (in Chinese with English Abstract)

    Google Scholar 

  • Zhang, Q., Zhou, Y. Z., 2017. Big Data Will Lead to a Profound Revolution in the Field of Geological Science. Chinese Journal of Geology, 52: 637–648 (in Chinese with English Abstract)

    Google Scholar 

  • Zhang, Z. W., Cai, K. Q., Xu, Z. H., 1999. Research Method of Large-Scale Metallogenic Prediction. Earth Science Frontiers, 6(1): 12 (in Chinese with English Abstract)

    Google Scholar 

  • Zhao, J. N., Chen, S. Y., Zuo, R. G., 2016. Identifying Geochemical Anomalies Associated with Au-Cu Mineralization Using Multifractal and Artificial Neural Network Models in the Ningqiang District, Shaanxi, China. Journal of Geochemical Exploration, 164: 54–64. https://doi.org/10.1016/j.gexplo.2015.06.018

    Article  Google Scholar 

  • Zhao, P. D., 2015. Digital Mineral Exploration and Quantitative Evaluation in the Big Data Age. Geological Bulletin of China, 34: 1255–1259 (in Chinese with English Abstract)

    Google Scholar 

  • Zhao, P. D., Chen, J. P., Zhang, S. T., 2003. The New Development of “Three Components” Quantitative Mineral Prediction. Earth Science Frontiers, 10: 455–463 (in Chinese with English Abstract)

    Google Scholar 

  • Zhao, P. D., Li, Z. J., Hu, G. D., 1992. Three-Dimensional Statistical Prediction of Deposit in Key Metallogenic Region: A Case Study of Yueshan Region in Anhui Province. China University of Geosciences Press, Wuhan, 107 (in Chinese)

    Google Scholar 

  • Zhao, Y., Yang, Q. J., 2019. Research on Hyperspectral Remote Sensing Image Classification Based on 3D Convolutional Neural Network. Information Techology and Network Security, 38: 46–51 (in Chinese with English Abstract)

    Google Scholar 

  • Zhou, Q., Du, Y. S., Qin, Y., 2013. Ancient Natural Gas Seepage Sedimentary-Type Manganese Metallogenic System and Ore-Forming Model: A Case Study of “Datangpo Type” Manganese Deposits Formed in Rift Basin of Nanhua Period along Guizhou-Hunan-Chongqing Border Area. Mineral Deposits, 32: 457–466 (in Chinese with English Abstract)

    Google Scholar 

  • Zhou, Q., Du, Y. S., Yuan, L. J., et al., 2016. The Structure of the Wuling Rift Basin and Its Control on the Manganese Deposit during the Nanhua Period in Guizhou-Hunan-Chongqing Border Area, South China. Earth Science, 41: 177–188 (in Chinese with English Abstract)

    Google Scholar 

  • Zhou, Y. Z., Chen, S., Zhang, Q., et al., 2018. Advances and Prospects of Big Data and Mathematical Geoscience. Acta Petrologica Sinica, 34: 255–263 (in Chinese with English Abstract)

    Google Scholar 

  • Zhou, Y. Z., Li, P. X., Wang, S. G., et al., 2017. Research Progress on Big Data and Intelligent Modelling of Mineral Deposits. Bulletin of Mineralogy, Petrology and Geochemistry, 36: 327–331 (in Chinese with English Abstract)

    Google Scholar 

  • Zhu, G. M., Zhang, L., Shen, P. Y., et al., 2019. Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM. IEEE Transactions on Multimedia, 21(4): 1011–1021. https://doi.org/10.1109/tmm.2018.2869278

    Article  Google Scholar 

  • Zuo, R. G., 2020. Geodata Science-Based Mineral Prospectivity Mapping: A Review. Natural Resources Research, 29(6): 3415–3424. https://doi.org/10.1007/s11053-020-09700-9

    Article  Google Scholar 

  • Zuo, R. G., Carranza, E. J. M., 2011. Support Vector Machine: A Tool for Mapping Mineral Prospectivity. Computers & Geosciences, 37(12): 1967–1975. https://doi.org/10.1016/j.cageo.2010.09.014

    Article  Google Scholar 

  • Zuo, R. G., Wang, Z. Y., 2020. Effects of Random Negative Training Samples on Mineral Prospectivity Mapping. Natural Resources Research, 29(6): 3443–3455. https://doi.org/10.1007/s11053-020-09668-6

    Article  Google Scholar 

  • Zuo, R. G., Xiong, Y. H., 2018. Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods. Natural Resources Research, 27(1): 5–13. https://doi.org/10.1007/s11053-017-9357-0

    Article  Google Scholar 

  • Zuo, R. G., 2019a. Deep Learning-Based Mining and Integration of deep-Level Mineralization Information. Bulletin of Mineralogy, Petrology and Geochemistry, 38: 53–60 (in Chinese with English Abstract)

    Google Scholar 

  • Zuo, R. G., 2019b. Exploration Geochemical Data Mining and Weak Geochemical Anomalies Identification. Earth Science Frontiers, 26: 67–75 (in Chinese with English Abstract)

    Google Scholar 

  • Zuo, R. G., Peng, Y., Li, T., et al., 2021. Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms. Earth Science, 46(1): 350–358. https://doi.org/10.3799/dqkx.2020.111

    Google Scholar 

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China

    Shi Li, Jianping Chen & Chang Liu

  2. Land Resources Information Development and Research Key Laboratory of Beijing, Beijing, 100083, China

    Shi Li, Jianping Chen & Chang Liu

  3. Haikou Marine Geological Survey Center, China Geological Survey, Haikou, 570100, China

    Yang Wang

Authors
  1. Shi Li
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  2. Jianping Chen
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  3. Chang Liu
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  4. Yang Wang
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Correspondence to Jianping Chen.

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Acknowledgments

This research is financially supported by the Chinese MOST project “Methods and Models for Quantitative Prediction of Deep Metallogenic Geological Anomalies” (No. 2017YFC0601502) and “Research on key technology of mineral prediction based on geological big data analysis” (No. 6142A01190104). The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1365-z.

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Li, S., Chen, J., Liu, C. et al. Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data. J. Earth Sci. 32, 327–347 (2021). https://doi.org/10.1007/s12583-020-1365-z

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  • DOI: https://doi.org/10.1007/s12583-020-1365-z

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