IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119318473.html
   My bibliography  Save this article

Elucidating the complexity of metallogenic elements based on multifractal detrending moving average analysis

Author

Listed:
  • Wan, Li
  • Zeng, Xiangjian
  • Lai, Jiajing

Abstract

Metallogenic element distributions generally present highly irregular spatial patterns and scale-dependent variations. In the present study, the multifractal detrending moving average (MFDMA) model is applied to describe the singularity spectrum of different metallogenic element distributions and study the heterogeneity of element concentrations in the Shangzhuang deposit, Jiaodong Province, China. The results show that six metallogenic elements exhibit multifractal scaling. The multifractal spectra of Au and Cu are left-deviated, while Ag, Pb, Zn, and Hg are close to symmetric. Hence, the shape of the multifractal spectrum may give directions to study the ore-forming potential. Notably, Au and Cu are unevenly distributed with stronger multifractal characteristics than other elements with significant difference, revealing that the distributions of orebodies are significantly inhomogeneous and that inflation and contraction phenomena are prominent. In contrast, Ag is a weak multifractal and nearly a single fractal, suggesting barely mineralized zones. When the sources of multifractality are quantified by two factors, long-range correlations, and broad fat-tail distributions, we find that the multifractal structure of Ag is because of the fat-tail probability density function, and the multifractal structure of the other elements is due to both factors.

Suggested Citation

  • Wan, Li & Zeng, Xiangjian & Lai, Jiajing, 2020. "Elucidating the complexity of metallogenic elements based on multifractal detrending moving average analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
  • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318473
    DOI: 10.1016/j.physa.2019.123296
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119318473
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2019.123296?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    2. Kaushik Matia & Yosef Ashkenazy & H. Eugene Stanley, 2003. "Multifractal Properties of Price Fluctuations of Stocks and Commodities," Papers cond-mat/0308012, arXiv.org.
    3. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
    Full references (including those not matched with items on IDEAS)

    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. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2014. "Multifractal detrended cross-correlation analysis of gold price and SENSEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 195-204.
    2. Sarker, Alivia & Mali, Provash, 2021. "Detrended multifractal characterization of Indian rainfall records," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    3. Ruan, Qingsong & Zhang, Manqian & Lv, Dayong & Yang, Haiquan, 2018. "SAD and stock returns revisited: Nonlinear analysis based on MF-DCCA and Granger test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1009-1022.
    4. Wang, Yudong & Wu, Chongfeng & Pan, Zhiyuan, 2011. "Multifractal detrending moving average analysis on the US Dollar exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3512-3523.
    5. Yuxin Zhao & Shuai Chang & Chang Liu, 2015. "Multifractal theory with its applications in data management," Annals of Operations Research, Springer, vol. 234(1), pages 133-150, November.
    6. Pal, Mayukha & Satish, B. & Srinivas, K. & Rao, P. Madhusudana & Manimaran, P., 2015. "Multifractal detrended cross-correlation analysis of coding and non-coding DNA sequences through chaos-game representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 596-603.
    7. Zhang, Chen & Ni, Zhiwei & Ni, Liping & Li, Jingming & Zhou, Longfei, 2016. "Asymmetric multifractal detrending moving average analysis in time series of PM2.5 concentration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 322-330.
    8. Cao, Guangxi & Xu, Wei, 2016. "Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 505-523.
    9. Ruan, Qingsong & Yang, Haiquan & Lv, Dayong & Zhang, Shuhua, 2018. "Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 243-256.
    10. Ruan, Qingsong & Zhou, Mi & Yin, Linsen & Lv, Dayong, 2021. "Hedging effectiveness of Chinese Treasury bond futures: New evidence based on nonlinear analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    11. Stošić, Darko & Stošić, Dusan & Stošić, Tatijana & Stanley, H. Eugene, 2015. "Multifractal analysis of managed and independent float exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 13-18.
    12. Delbianco, Fernando & Tohmé, Fernando & Stosic, Tatijana & Stosic, Borko, 2016. "Multifractal behavior of commodity markets: Fuel versus non-fuel products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 573-580.
    13. Darko Stosic & Dusan Stosic & Irena Vodenska & H. Eugene Stanley & Tatijana Stosic, 2021. "A new look at calendar anomalies: Multifractality and day of the week effect," Papers 2106.06164, arXiv.org.
    14. Ruan, Yong-Ping & Zhou, Wei-Xing, 2011. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1646-1654.
    15. Zhou, Wei-Xing, 2012. "Finite-size effect and the components of multifractality in financial volatility," Chaos, Solitons & Fractals, Elsevier, vol. 45(2), pages 147-155.
    16. Ruan, Qingsong & Bao, Junjie & Zhang, Manqian & Fan, Limin, 2019. "The effects of exchange rate regime reform on RMB markets: A new perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 122-134.
    17. Wang, Dong-Hua & Yu, Xiao-Wen & Suo, Yuan-Yuan, 2012. "Statistical properties of the yuan exchange rate index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3503-3512.
    18. Li Wang & Xing-Lu Gao & Wei-Xing Zhou, 2023. "Testing for intrinsic multifractality in the global grain spot market indices: A multifractal detrended fluctuation analysis," Papers 2306.10496, arXiv.org.
    19. Ruan, Qingsong & Meng, Lu & Lv, Dayong, 2021. "Effect of introducing Bitcoin futures on the underlying Bitcoin market efficiency: A multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    20. Shen, Na & Chen, Jiayi, 2023. "Asymmetric multifractal spectrum distribution based on detrending moving average cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).

    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:eee:phsmap:v:541:y:2020:i:c:s0378437119318473. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.