[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The world has moved into an era of hidden ore body exploration, necessitating the development of new prospecting and exploration methods. One promising approach is to use the deep convolutional neural network (DCNN) algorithm to extract spatial and correlation characteristics of multiple two-dimensional elements related to hidden ores. This paper explores this method on Datangpo manganese (Mn), constructing prediction datasets that includes geological, geochemical, geophysical and aeromagnetic features. Analyzing metallogenic conditions and control factors of Mn ores, we construct a Mn ore prediction model (Geo-DCNN) based on multiple geographical knowledge and DCNN. The Geo-DCNN model reaches ore-bearing accuracy of 79.11%, non-ore-bearing accuracy of 99.01%, overall accuracy of 95.35%, and loss value of 0.0227 after training. Based on analysis of ROC curve, P-R curve, field investigation, and target area verification, we discover that the prediction results of the Geo-DCNN model in northeast Guizhou have a high correspondence rate with known manganese deposits. This provides valuable insight for further ore exploration in the area. Additionally, the results indicate that the Geo-DCNN model is robust and portable, suggesting that it can be applied to metallogenic prediction practices for manganese ore in similar regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All data used during the study are not available.

Code availability

The code is not available.

References

  • Abedi M, Norouzi GH, Bahroudi A (2012) Support vector machine for multiclassification of mineral prospectivity areas. Comput Geosci 46(2):272–283

    CAS  ADS  Google Scholar 

  • Bai H, Cao Y, Zhang H, Zhang C, Hou S, Wang W (2021) Combining fuzzy analytic hierarchy process with concentration–area fractal for mineral prospectivity mapping: A case study involving Qinling orogenic belt in central China. Appl Geochem 126:104894

    CAS  Google Scholar 

  • Blanchard G, Lee G, Scott C (2010) Semi-supervised novelty detection. J Mach Learn Res 11(11):2973–3009

    MathSciNet  Google Scholar 

  • Bradley A (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159

    ADS  Google Scholar 

  • Buyrukoğlu S, Savaş S (2023) Stacked-based ensemble machine learning model for positioning footballer. Arab J Sci Eng 48(2):1371–1383

    Google Scholar 

  • Carranza EJM (2009) Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Comput Geosci 35:2032–2046

    ADS  Google Scholar 

  • Carranza EJM (2011) Geocomputation of mineral exploration targets. Comput Geosci 37(12):1907–1916

    ADS  Google Scholar 

  • Carranza EJM, Laborte AG (2015) Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm. Ore Geol Rev 71:777–787

    Google Scholar 

  • Chen YL, Wu W (2017) Mapping mineral prospectivity using an extreme learning machine regression. Ore Geol Rev 80:200–213

    Google Scholar 

  • Chen YL, Lu L, Li X (2014) Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. J Geochem Explor 140:56–63

    CAS  Google Scholar 

  • Chen Q, Liu G, Ma X, Li X, He Z (2020) 3D stochastic modeling framework for Quaternary sediments using multiple-point statistics: A case study in Minjiang Estuary area. Southeast China Comput Geosci 136:104404

    Google Scholar 

  • Chen GX, Huang N, Wu GP, Luo L, Wang DT, Cheng QM (2022) Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province. Ore Geol Rev 143:104765

    Google Scholar 

  • Chen Q, Liu G, Ma X, Que X (2023) Spatial Analysis. In: Daya Sagar BS, Cheng Q, McKinley J, Agterberg F (eds) Encyclopedia of Mathematical Geosciences, Springer Nature

  • Daviran M, Maghsoudi A, Ghezelbash R, Pradhan B (2021) A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Comput Geosci 148:104688

    CAS  Google Scholar 

  • Daviran M, Parsa M, Maghsoudi A, Ghezelbash R (2022) Quantifying uncertainties linked to the diversity of mathematical frameworks in knowledge-driven mineral prospectivity mapping. Nat Resour Res 31(5):2271–2287

    Google Scholar 

  • Deng H, Wang YF, Chen J, Liu ZK, Yu SY, Mao XC (2021) Three-dimensional prospectivity mapping and quantitative analysis of structural ore-controlling factors in Jiaojia Au ore-belt with attention convolutional neural networks. J Cent South Univ 52(9):3003–3014

    Google Scholar 

  • Elyasi GR, Bahroudi A, Abedi M (2019) Risk-based analysis in mineral potential mapping: Application of quantifier-guided ordered weighted averaging method. Nat Resour Res 28(3):931–951

    Google Scholar 

  • Fatehi M, Asadi HH (2017) Data integration modeling applied to drillhole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran. J Afr Earth Sci 128:147–160

    CAS  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    MathSciNet  ADS  Google Scholar 

  • Ford A (2020) Practical implementation of random forest-based mineral potential mapping for porphyry Cu-Au mineralization in the Eastern Lachlan Orogen, NSW. Australia Nat Resour Res 29(1):267–283

    CAS  Google Scholar 

  • Fu C, Chen K, Yang Q, Chen J, Wang J, Liu J, Xiang Y, Li Y, Rajesh HM (2021) Mapping gold mineral prospectivity based on weights of evidence method in southeast Asmara. Eritrea J Afr Earth Sci 176:104143

    CAS  Google Scholar 

  • Ghezelbash R, Maghsoudi A, Bigdeli A, Carranza EJM (2021) Regional-scale mineral prospectivity mapping: Support vector machines and an improved data driven multi-criteria decision-making technique. Nat Resour Res 30(3):1977–2005

    CAS  Google Scholar 

  • Hajihosseinlou M, Maghsoudi A, Ghezelbash R (2024) Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran. Expert Syst Appl 237:121668

    Google Scholar 

  • Han G, Zhu M, Zhao X, Gao H (2021) Method based on the cross-layer attention mechanism and multiscale perception for safety helmet-wearing detection. Comput Electr Eng 95:107458

    Google Scholar 

  • Karpatne A, Ebert-Uphoff I, Ravela S, Babaie HA, Kumar V (2018) Machine learning for the geosciences: Challenges and opportunities. IEEE T Knowl Data En 31:1544–1554

    Google Scholar 

  • Ke T, Jing L, Lv H, Zhang L, Hu Y (2018) Global and local learning from positive and unlabeled examples. Appl Intell 48:2373–2392

    Google Scholar 

  • Li TF, Xia QL, Zhao MY, Gui Z, Leng S (2020) Prospectivity mapping for tungsten polymetallic mineral resources, Nanling Metallogenic Belt, South China: Use of Random Forest algorithm from a perspective of data imbalance. Nat Resour Res 29(1):203–227

    CAS  Google Scholar 

  • Li S, Chen J, Liu C, Wang Y (2021) Mineral prospectivity prediction via convolutional neural networks based on geological big data. J Earth Sci 32:327–347

    Google Scholar 

  • Lin N, Chen Y, Liu H, Liu H (2021) A comparative study of machine learning models with hyperparameter optimization algorithm for mapping mineral prospectivity. Minerals 11(2):159

    CAS  ADS  Google Scholar 

  • Lisitsin VA, Porwal A, McCuaig TC (2014) Probabilistic fuzzy logic modeling: Quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations. Math Geosci 46(6):747–769

    MathSciNet  Google Scholar 

  • Liu Y, Zhou K, Xia Q (2018) A MaxEnt model for mineral prospectivity mapping. Nat Resour Res 27(3):299–313

    Google Scholar 

  • Luo Z, Xiong Y, Zuo R (2020) Recognition of geochemical anomalies using a deep variational autoencoder network. Appl Geochem 122:104710

    CAS  Google Scholar 

  • Ma JQ, Kong ZSM, FH, (2022) Semantic image segmentation by using multi-scale strip pooling and channel attention. J Image Graph 27(12):3530–3541

    Google Scholar 

  • Maepa F, Smith RS, Tessema A (2021) Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario. Canada Ore Geol Rev 130:103968

    Google Scholar 

  • Mao XC, Wang Q, Chen J, Deng H, Liu ZK, Wang JL, Chen JP, Xiao KY (2020) Three-dimensional modeling of deep metallogenic structure in northwestern Jiaodong Peninsula and its gold prospecting significance. Acta Geosci Sinica 41(2):166–178

    Google Scholar 

  • Nykänen V (2008) Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the central lapland greenstone belt, northern Fennoscandian Shield. Nat Resour Res 17(1):29–48

    Google Scholar 

  • Nykänen V, Niiranen T, Molnar F, Lahti I, Korhonen K, Cook N (2017) Optimizing a knowledge-driven prospectivity model for gold deposits within Perapohja Belt. Northern Finland Nat Resour Res 26(4):1–14

    Google Scholar 

  • Oh HJ, Lee S (2010) Application of artificial neural network for gold-silver deposits potential mapping: a case study of Korea. Nat Resour Res 19(2):103–124

    CAS  Google Scholar 

  • Pan GC, Harris DP (2000) Information Synthesis for Mineral Exploration. Oxford University Press, NewYork, p 461

    Google Scholar 

  • Parsa M, Carranza EJM, Ahmadi B (2021) Deep GMDH neural networks for predictive mapping of mineral prospectivity in terrains hosting few but large mineral deposits. Nat Resour Res 31:37–50

    Google Scholar 

  • Pichler O, Teuner A, Hosticka BJ (1996) A comparison of texture feature extraction using adaptive Gabor filtering, Pyramidal and tree structured wavelet transforms. Pattern Recogn 29(5):733–742

    ADS  Google Scholar 

  • Porwal A, Carranza EJM, Hale M (2006) Bayesian network classifiers for mineral potential mapping. Comput Geosci 32(1):1–16

    ADS  Google Scholar 

  • Qin Y, Liu L, Wu W (2021) Machine learning-based 3D modeling of mineral prospectivity mapping in the Anqing Orefield. Eastern China Nat Resour Res 30(5):3099–3120

    Google Scholar 

  • Rigol-Sanchez JP, Chica-Olmo M, Barca-Hernandez F (2003) Artificial neural networks as a tool for mineral potential mapping with GIS. Int J Remote Sens 24(5):1151–1156

    Google Scholar 

  • Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas MJOGR (2015) Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818

    Google Scholar 

  • Sadeghi B, Porwal A, Pour AB, Rahimzadegan M (2023) Fuzzy set theory in Geosciences. In: Daya Sagar BS, Cheng Q, McKinley J, Agterberg F (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series Springer, Cham, 454–463

  • Saljoughi BS, Hezarkhani A (2016) A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) datadriven models to mineral potential mapping for copper mineralizations in the Shahre-Babak region, Kerman. Iran Appl Geomat 10(3):229–256

    Google Scholar 

  • Sun T, Chen F, Zhong L, Liu W, Wang Y (2019) GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geol Rev 109:26–49

    Google Scholar 

  • Sun T, Li H, Wu K, Chen F, Zhu Z, Hu Z (2020) Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from Southern Jiangxi Province. China Minerals 10:102

    ADS  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    MathSciNet  CAS  PubMed  ADS  Google Scholar 

  • Tao JT, Yuan F, Zhang NN, Chang JY (2021) Three-dimensional prospectivity modeling of Honghai volcanogenic massive sulfide Cu-Zn deposit, eastern Tianshan, northwestern China using weights of evidence and fuzzy logic. Math Geosci 53:131–162

    CAS  Google Scholar 

  • Tayebi MH, Tangestani MH, Vincent RK (2014) Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model. Turk J Earth Sci 23(6):627–644

    Google Scholar 

  • Tsokos CP, Welch RLW (1978) Bayes discrimination with mean square error loss. Pattern Recogn 10(2):113–123

    ADS  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Advances in Neural Information Pro-cessing Systems, Long Beach. Curran Associates Inc, USA, pp 5998–6008

    Google Scholar 

  • Wan JW, Yang M (2020) Survey on cost-sensitive learning method. J Softw 31(1):113–136

    Google Scholar 

  • Wu C, Zhou Q, Xu K, Zhang X, Kong C, Li Y, Yang B, Zhang S, Yuan L (2022) A review study of the prospecting process of Datangpo Manganese ore used for Big Data prediction. Guizhou Geol 39(3):189–204

    Google Scholar 

  • Xiang J, Xiao K, Carranza EJM, Chen J, Li S (2020) 3D mineral prospectivity mapping with random forests: A case study of Tongling, Anhui, China. Nat Resour Res 29:395–414

    Google Scholar 

  • Xiao F, Chen JG, Hou WS, Wang ZH, Zhou YZ, Erten O (2018) A spatially weighted singularity mapping method applied to identify epithermal Ag and Pb-Zn polymetallic mineralization associated geochemical anomaly in northwest Zhejiang, China. J Geochem Explor 189:122–137

    CAS  Google Scholar 

  • Xiao F, Wang KQ, HouWS WZH, Zhou YZ (2020) Prospectivity mapping for porphyry Cu-Mo mineralization in the Eastern Tianshan, Xinjiang, Northwestern China. Nat Resour Res 29:89–113

    CAS  Google Scholar 

  • Xiao F, Chen W, Wang J, Erten O (2022) A hybrid logistic regression: Gene expression programming model and its application to mineral prospectivity mapping. Nat Resour Res 31:2041–2064

    Google Scholar 

  • Xiao F, Cheng QM, Hou WS, Agterberg F (2023) Three-dimensional prospectivity modeling of Jinshan Ag-Au deposit, southern China by weights-of-evidence. J Earth Sci. https://doi.org/10.1007/s12583-023-1822-6

    Article  Google Scholar 

  • Xiong Y, Zuo R (2018) GIS-based rare events logistic regression for mineral prospectivity mapping. Comput Geosci 111:18–25

    CAS  ADS  Google Scholar 

  • Xu K, Yuan LJ, Yang BN, Kong CF, Zhang XL, Zheng J, Zhou Q, Wu CL (2020) Extraction of hidden manganese ore information with combined mining of associated and secondary mineral of remote sensing data on northeast Guizhou. Bull Geol Sci Technol 39(4):37–43

    CAS  Google Scholar 

  • Xu Y, Li Z, Xie Z, Cai H, Niu P, Liu H (2021) Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area. Gansu Ore Geol Rev 138:104316

    Google Scholar 

  • Yang N, Zhang Z, Yang J, Hong Z, Shi J (2021) A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation. Nat Resour Res 30(6):3905–3923

    Google Scholar 

  • Yellishetty M, Mudd GM, Ranjith PG (2011) The steel industry, abiotic resource depletion and life cycle assessment: A real or perceived issue? J Clean Prod 19:78–90

    CAS  Google Scholar 

  • Yin JN, Li N (2022) Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping. Ore Geol Rev 145:104916

    Google Scholar 

  • Yousefi M, Carranza EJM (2016) Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Nat Resour Res 25(1):3–18

    CAS  Google Scholar 

  • Yousefi M, Kreuzer OP, Nykänen V, Hronsky JMA (2019) Exploration information systems–a proposal for the future use of GIS in mineral exploration targeting. Ore Geol Rev 111:103005

    Google Scholar 

  • Yousefi M, Carranza EJM, Kreuzer OP, Nykänen V, Hronsky JMA, Mihalasky MJ (2021) Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: state-of-the-art and outlook. J Geochem Explor 229:106839

    CAS  Google Scholar 

  • Yuan LJ, Zhou Q, Yao XC, Xie XF, Du YS, Zhang S, Pan W, Xie XY, Wang P, Xu Y (2018) Main geological characteristics of Songtao Gaodi extra-large manganese-rich deposit in Guizhou. Guizhou Geol 35(4):314–318

    Google Scholar 

  • Zhang N, Zhou K (2015) Mineral prospectivity mapping with weights of evidence and fuzzy logic methods. J Intell Fuzzy Syst 29(6):2639–2651

    Google Scholar 

  • Zhang Z, Zuo R, Xiong Y (2016) 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 Sci China Earth Sci 59(3):556–572

    CAS  ADS  Google Scholar 

  • Zhang N, Zhou K, Du X (2017) Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China. J Afr Earth Sci 128:84–96

    CAS  Google Scholar 

  • Zhang S, Zhou Q, Zhang PY, Sheng HQ, Yuan LJ, Qin YJ (2018) Geological Characteristics and Prospecting Practice of Pujue Superlarge Manganese deposit in Songtao Guizhou. Guizhou Geol 35(4):304–313

    Google Scholar 

  • Zhang S, Carranza EJM, Wei H, Xiao K, Yang F, Xiang J, Zhang S, Xu Y (2021) Data driven mineral prospectivity mapping by joint application of unsupervised convolutional auto-encoder network and supervised convolutional neural network. Nat Resour Res 30(2):1011–1031

    Google Scholar 

  • Zhang QP, Chen JP, Xu H, JiaYL CXW, Jia Z, Liu H (2022) Three-dimensional mineral prospectivity mapping by XGBoost modeling: A case study of the Lannigou gold deposit. Cina Nat Resour Res 31(3):1135–1156

    CAS  Google Scholar 

  • Zhao S, Zhao L (2021) On image recognition using Bidirectional Feature Pyramid and deep neural network. J Harbin Univ Sci Technol 26(2):44–50

    Google Scholar 

  • Zhou Q, Du YS, Yuan LJ, Zhang S, Yu WC, Yang ST, Liu Y (2016) The structure of the Wuling rift basin and its control on the manganese deposit during the Nanhuanian in Guizhou-Hunan-Chongqing border area. South China Earth Sci 41(2):177–188

    Google Scholar 

  • Zhou Q, Du YS, Yuan LJ, Zhang S, Yang BN, Pan W, Yu WC, Wang P, Xu Y, Qi L, Liu Y, Qin YJ, Xie XF (2017) Exploration models of ancient natural gas seep sedimentary-type manganese ore deposit: A case study of the Nanhuanian “Datangpo” type manganese ore in the conjunction area of Guizhou. Hunan and Chongqing Acta Geol Sinica 91(10):2285–2298

    Google Scholar 

  • Zhou Q, Du YS, Yuan LJ, Zhang S, Yu WC, Xie XF, Yang BN (2018) Research history, Major progress and outlook of ‘Datangpo type’ manganese deposits in Nanhuanian of east Guizhou and nearby area. Guizhou Geol 35(4):270–281

    Google Scholar 

  • Zhou Q, Wu C, Hu X, Yang B, Zhang X, Du Y, Xu K, Yuan L, Ni J, Hu D, Zhang S, Shen X, Liu Y, Xie X, Wang J (2022) A New Metallogenic Model for the Giant Manganese Deposits in Northeastern Guizhou, China. Ore Geol Rev 149:105070

    Google Scholar 

  • Zuo R, Carranza EJM (2011) Support vector machine: A tool for mapping mineral prospectivity. Comput Geosci 37(12):1967–1975

    CAS  ADS  Google Scholar 

  • Zuo R, Luo Z, Xiong Y, Yin B (2022) A geologically constrained variational autoencoder for mineral prospectivity mapping. Nat Resour Res 31(3):1121–1133

    Google Scholar 

  • Zuo R, Xu Y (2022) Graph deep learning model for mapping mineral prospectivity. Math Geosci 1–21

Download references

Acknowledgements

The authors would like to thank the Guangxi Bureau of Land and Resources for providing the various datasets used in this paper.

Funding

This work has been supported by the National Natural Science Foundation of China (No:41201193); Hubei Provincial Natural Science Foundation of China (No:2021CFB506); Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (No:KLIGIP-2023-B08); Research and Development Base for Deep Prediction and Exploration Technology of Manganese Mineral Resources [2021]4027; Science and Technology Plan Project of Guizhou Province [2020]4Y039; and Science and Technology Strategic Prospecting Project of Guizhou Province [2022] ZD003 and [2022] ZD004. The authors would like to thank the anonymous reviewers for providing valuable comments on the manuscript.

Author information

Authors and Affiliations

  1. School of Computer, China University of Geosciences, Wuhan, 430074, China

    Kai Xu, Siyuan Zhao, Chonglong Wu, Changyu Yang & Chunfang Kong

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China

    Kai Xu & Chunfang Kong

  3. Technology Innovation Center of Mineral Resources Explorations Engineering in Bedrock Zones, Ministry of Natural Resources, Guiyang, 550081, China

    Kai Xu, Chonglong Wu & Chunfang Kong

  4. Guizhou Key Laboratory for Strategic Mineral Intelligent Exploration, Guiyang, 550081, China

    Kai Xu

  5. Wuhan Dida Quanty Science & Technology Co., Ltd., Wuhan, 430205, China

    Kai Xu, Chonglong Wu, Yan Li, Yang Dong, Yongjin Wu, Shize Xiang & Chunfang Kong

  6. 103 Geological Team, Guizhou Geological and Mineral Exploration Bureau, Tongren, 554300, China

    Sui Zhang & Liangjun Yuan

Authors
  1. Kai Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siyuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chonglong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liangjun Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Changyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yang Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yongjin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shize Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chunfang Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first author contribution statement: Kai Xu: Conceptualization, Methodology, Investigation, Writing—original draft, Writing—review & editing. Siyuan Zhao: Material preparation, Methodology, Software. Chonglong Wu, Sui Zhang, and Liangjun Yuan: Methodology, Investigation, and Supervision. Changyu Yang: Software, Data collection and analysis. Yan Li, Yang Dong, Yongjin Wu, and Shize Xiang: Software and Data collection. Credit author: Chunfang Kong: Investigation, Writing—original draft, Writing—review & editing, Supervision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chunfang Kong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Communicated by: Xiaogang Ma

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, K., Zhao, S., Wu, C. et al. Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou. Earth Sci Inform 17, 1681–1697 (2024). https://doi.org/10.1007/s12145-024-01224-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-024-01224-7

Keywords

Search

Navigation