IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v85y2017i1d10.1007_s11069-016-2558-8.html
   My bibliography  Save this article

Fractal analysis of the ground-recorded ULF magnetic fields prior to the 11 March 2011 Tohoku earthquake (M W = 9): discriminating possible earthquake precursors from space-sourced disturbances

Author

Listed:
  • Stelios M. Potirakis

    (Piraeus University of Applied Sciences (TEI of Piraeus))

  • Masashi Hayakawa

    (UEC (University of Electro-Communications) Incubation Center
    UEC, Advanced Wireless Communications Research Center)

  • Alexander Schekotov

    (UEC, Advanced Wireless Communications Research Center
    Russian Academy of Sciences)

Abstract

The fractal characteristics of the ultra-low-frequency (ULF) magnetic field variations recorded prior to the Tohoku earthquake (EQ) with M W = 9 which happened on 11 March 2011 are studied in this article with the use of detrended fluctuation analysis and Higuchi fractal dimension algorithm. In the specific study, we use for our calculations only nighttime (LT = 3 a.m. ± 2 h) data because of their lowest contamination by industrial noise. A key aspect of our analysis is the investigation about any possible correlation of the ULF magnetic field variations or their calculated fractal characteristics with geomagnetic indices. Different preprocessing approaches are examined aiming at the minimization of any possible influences from global phenomena in the fractal analysis results, while in the same time retaining the scale-invariant character of ULF magnetic field variations after preprocessing. The obtained fractal analysis results imply locally driven change in the fractal characteristics of the ULF data prior to the Tohoku EQ, which is compatible with the change that has been reported prior to other large EQs.

Suggested Citation

  • Stelios M. Potirakis & Masashi Hayakawa & Alexander Schekotov, 2017. "Fractal analysis of the ground-recorded ULF magnetic fields prior to the 11 March 2011 Tohoku earthquake (M W = 9): discriminating possible earthquake precursors from space-sourced disturbances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(1), pages 59-86, January.
    • Handle: RePEc:spr:nathaz:v:85:y:2017:i:1:d:10.1007_s11069-016-2558-8
    DOI: 10.1007/s11069-016-2558-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2558-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-016-2558-8?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. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    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. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    2. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
    3. repec:rdg:wpaper:em-dp2013-04 is not listed on IDEAS
    4. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    5. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    6. Th I Götz & G Lahmer & V Strnad & Ch Bert & B Hensel & A M Tomé & E W Lang, 2017. "A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-31, September.
    7. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    8. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    9. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    10. Carlos Alberto Orge Pinheiro & Valter de Senna, 2016. "Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BMeFbovespa," Brazilian Business Review, Fucape Business School, vol. 13(5), pages 129-157, September.
    11. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
    12. Zhang, Kequan & Qu, Zongxi & Dong, Yunxuan & Lu, Haiyan & Leng, Wennan & Wang, Jianzhou & Zhang, Wenyu, 2019. "Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 814-830.
    13. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    14. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    15. Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    16. Najafi, Nadia & Paulsen, Uwe Schmidt, 2017. "Operational modal analysis on a VAWT in a large wind tunnel using stereo vision technique," Energy, Elsevier, vol. 125(C), pages 405-416.
    17. Marinoiu Cristian, 2018. "Average Monthly Temperature Forecast In Romania By Using Singular Spectrum Analysis," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 3, pages 48-57, June.
    18. Christina Beneki & Bruno Eeckels & Costas Leon, 2012. "Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(5), pages 391-400, August.
    19. Andrea Saayman & Jacques de Klerk, 2019. "Forecasting tourist arrivals using multivariate singular spectrum analysis," Tourism Economics, , vol. 25(3), pages 330-354, May.
    20. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli Segnon, 2017. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 83-97, January.
    21. Cheng-Hong Yang & Jen-Chung Shao & Yen-Hsien Liu & Pey-Huah Jou & Yu-Da Lin, 2022. "Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes," Mathematics, MDPI, vol. 10(14), pages 1-18, July.

    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:spr:nathaz:v:85:y:2017:i:1:d:10.1007_s11069-016-2558-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.