IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v637y2024ics0378437124000906.html
   My bibliography  Save this article

Multifractal detrended fluctuation analysis of soil radon in the Kachchh Region of Gujarat, India: A case study of earthquake precursors

Author

Listed:
  • Sahoo, Sushanta Kumar
  • Katlamudi, Madhusudhanarao
  • Pedapudi, Chandra Sekhar

Abstract

In this paper, the scaling properties of earthquake-induced soil radon emissions in the Kachchh region of Gujarat, India, were demonstrated through the application of multifractal detrended fluctuation analysis (MFDFA). The analysis considered soil radon data recorded at four stations, namely Rampar, Rapar, Pragpar and Paddhar, over a period of one year (January 1, 2020 to December 31, 2020). At these stations, soil radon along with soil pressure and temperature are recorded continuously at 10-minute intervals. In the first step, periodic oscillations such as diurnal and semi-diurnal periodicities of the raw soil radon data were removed by applying empirical mode decomposition (EMD). The aperiodic soil radon at all measuring stations was used as input for the MFDFA analysis. In order to obtain the exact source of the multifractality, the MFDFA is also applied to the shuffled and surrogate data of the aperiodic soil radon time series at all measuring stations. It can be observed that the fluctuation function (Log (Fq(s))) for all measuring stations increases linearly with increasing scale value(s). In contrast, the generalized Hurst exponent (H(q)) of the shuffled radon values is about 0.5, the H(q) value of the surrogate and observed radon series is greater than 0.5 at all measuring stations. The scaling exponent τqof shuffled Radon shows an approximately linear trend, while surrogate and soil observed radon show a non-linear trend. At all stations, the shuffled series shows a narrower spectrum compared to the surrogate and observed soil radon time series. This shows that observed soil radon has a long-range correlation. Three local earthquakes of June 14, 2020 (M=5.3), August 28, 2020 (M=4.1) and November 1, 2020 (M=4.1) near monitoring stations are considered for analysis in this study. The multifractal spectrum of soil radon is also examined during the seismically quiet and disturbed period. A broader spectrum is obtained during the disturbed period compared to the quiet period. This may be due to heterogeneity characterized by anomalous emission of soil radon gas before the occurrence of earthquakes during the monitoring period.

Suggested Citation

  • Sahoo, Sushanta Kumar & Katlamudi, Madhusudhanarao & Pedapudi, Chandra Sekhar, 2024. "Multifractal detrended fluctuation analysis of soil radon in the Kachchh Region of Gujarat, India: A case study of earthquake precursors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    • Handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124000906
    DOI: 10.1016/j.physa.2024.129582
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124000906
    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.2024.129582?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. Saheli Chowdhury & Argha Deb & Md. Nurujjaman & Chiranjib Barman, 2017. "Identification of pre-seismic anomalies of soil radon-222 signal using Hilbert–Huang transform," 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. 87(3), pages 1587-1606, July.
    2. Zhang, Chen & Ni, Zhiwei & Ni, Liping, 2015. "Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 114-123.
    3. Jaros{l}aw Kwapie'n & Pawel Blasiak & Stanis{l}aw Dro.zd.z & Pawe{l} O'swik{e}cimka, 2022. "Genuine multifractality in time series is due to temporal correlations," Papers 2211.00728, arXiv.org, revised Mar 2023.
    4. 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.
    5. Aggarwal, S.K. & Lovallo, Michele & Khan, P.K. & Rastogi, B.K. & Telesca, Luciano, 2015. "Multifractal detrended fluctuation analysis of magnitude series of seismicity of Kachchh region, Western India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 426(C), pages 56-62.
    6. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
    7. Thompson, James R. & Wilson, James R., 2016. "Multifractal detrended fluctuation analysis: Practical applications to financial time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 126(C), pages 63-88.
    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. Wang, Hong-Yong & Wang, Tong-Tong, 2018. "Multifractal analysis of the Chinese stock, bond and fund markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 280-292.
    2. Olivares, Felipe & Zanin, Massimiliano, 2022. "Corrupted bifractal features in finite uncorrelated power-law distributed data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    5. Vitanov, Nikolay K. & Sakai, Kenshi & Dimitrova, Zlatinka I., 2008. "SSA, PCA, TDPSC, ACFA: Useful combination of methods for analysis of short and nonstationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 187-202.
    6. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    7. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    8. Laura Raisa Miloş & Cornel Haţiegan & Marius Cristian Miloş & Flavia Mirela Barna & Claudiu Boțoc, 2020. "Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets," Sustainability, MDPI, vol. 12(2), pages 1-15, January.
    9. Nagarajan, Radhakrishnan & Kavasseri, Rajesh G., 2005. "Minimizing the effect of trends on detrended fluctuation analysis of long-range correlated noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 182-198.
    10. Meo, Marcos M. & Iaconis, Francisco R. & Del Punta, Jessica A. & Delrieux, Claudio A. & Gasaneo, Gustavo, 2024. "Multifractal information on reading eye tracking data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    11. 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.
    12. Jiang, Lei & Zhang, Jiping & Liu, Xinwei & Li, Fei, 2016. "Multi-fractal scaling comparison of the Air Temperature and the Surface Temperature over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 783-792.
    13. Jiang, Jiaqi & Gu, Rongbao, 2016. "Asymmetrical long-run dependence between oil price and US dollar exchange rate—Based on structural oil shocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 75-89.
    14. Zeng, Yayun & Wang, Jun & Xu, Kaixuan, 2017. "Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 364-376.
    15. Michalski, Sebastian, 2008. "Blocks adjustment—reduction of bias and variance of detrended fluctuation analysis using Monte Carlo simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 217-242.
    16. Jamshid Ardalankia & Mohammad Osoolian & Emmanuel Haven & G. Reza Jafari, 2019. "Scaling Features of Price-Volume Cross-Correlation," Papers 1903.01744, arXiv.org, revised Aug 2020.
    17. Longfeng Zhao & Wei Li & Chunbin Yang & Jihui Han & Zhu Su & Yijiang Zou, 2017. "Multifractality and Network Analysis of Phase Transition," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    18. Kumiko Tanaka-Ishii & Armin Bunde, 2016. "Long-Range Memory in Literary Texts: On the Universal Clustering of the Rare Words," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    19. Kalamaras, N. & Philippopoulos, K. & Deligiorgi, D. & Tzanis, C.G. & Karvounis, G., 2017. "Multifractal scaling properties of daily air temperature time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 38-43.
    20. He, Hong-di & Wang, Jun-li & Wei, Hai-rui & Ye, Cheng & Ding, Yi, 2016. "Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 518-525.

    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:637:y:2024:i:c:s0378437124000906. 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.