IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i12p3345-d240466.html
   My bibliography  Save this article

Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

Author

Listed:
  • Guowei Liu

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fengshan Ma

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Gang Liu

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Haijun Zhao

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Jie Guo

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Jiayuan Cao

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

Abstract

Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na + and CHO 3 − concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

Suggested Citation

  • Guowei Liu & Fengshan Ma & Gang Liu & Haijun Zhao & Jie Guo & Jiayuan Cao, 2019. "Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China," Sustainability, MDPI, vol. 11(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3345-:d:240466
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/12/3345/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/12/3345/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rafael Epstein & Marcel Goic & Andrés Weintraub & Jaime Catalán & Pablo Santibáñez & Rodolfo Urrutia & Raúl Cancino & Sergio Gaete & Augusto Aguayo & Felipe Caro, 2012. "Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines," Operations Research, INFORMS, vol. 60(1), pages 4-17, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jia Liu & Fengshan Ma & Jie Guo & Guang Li & Yewei Song & Yang Wan, 2022. "A Field Study on the Law of Spatiotemporal Development of Rock Movement of Under-Sea Mining, Shandong, China," Sustainability, MDPI, vol. 14(10), pages 1-13, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. César Flores-Fonseca & Rodrigo Linfati & John Willmer Escobar, 2022. "Exact algorithms for production planning in mining considering the use of stockpiles and sequencing of power shovels in open-pit mines," Operational Research, Springer, vol. 22(3), pages 2529-2553, July.
    2. Martin L. Smith & Stewart J. Wicks, 2014. "Medium-Term Production Scheduling of the Lumwana Mining Complex," Interfaces, INFORMS, vol. 44(2), pages 176-194, April.
    3. Pérez, Juan & Maldonado, Sebastián & González-Ramírez, Rosa, 2018. "Decision support for fleet allocation and contract renegotiation in contracted open-pit mine blasting operations," International Journal of Production Economics, Elsevier, vol. 204(C), pages 59-69.
    4. Michelle L. Blom & Christina N. Burt & Adrian R. Pearce & Peter J. Stuckey, 2014. "A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 658-676, November.
    5. Foroughi, Sorayya & Hamidi, Jafar Khademi & Monjezi, Masoud & Nehring, Micah, 2019. "The integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    6. Thomas W. M. Vossen & R. Kevin Wood & Alexandra M. Newman, 2016. "Hierarchical Benders Decomposition for Open-Pit Mine Block Sequencing," Operations Research, INFORMS, vol. 64(4), pages 771-793, August.
    7. Chung, Joyce & Asad, Mohammad Waqar Ali & Topal, Erkan, 2022. "Timing of transition from open-pit to underground mining: A simultaneous optimisation model for open-pit and underground mine production schedules," Resources Policy, Elsevier, vol. 77(C).
    8. Zhang, Jian & Nault, Barrie R. & Dimitrakopoulos, Roussos G., 2019. "Optimizing a mineral value chain with market uncertainty using benders decomposition," European Journal of Operational Research, Elsevier, vol. 274(1), pages 227-239.
    9. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    10. Michelle L. Blom & Adrian R. Pearce & Peter J. Stuckey, 2016. "A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks Over Multiple Time Periods," Management Science, INFORMS, vol. 62(10), pages 3059-3084, October.
    11. Marco Schulze & Julia Rieck & Cinna Seifi & Jürgen Zimmermann, 2016. "Machine scheduling in underground mining: an application in the potash industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(2), pages 365-403, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3345-:d:240466. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.