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تحلیل آنتروپی چندمقیاسه و همبستگی بلندبرد چرخههای مختلف لکههای خورشیدی | ||
| فیزیک زمین و فضا | ||
| مقاله 7، دوره 52، شماره 1، خرداد 1405، صفحه 107-120 اصل مقاله (875.51 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2025.398875.1007710 | ||
| نویسنده | ||
| محسن جواهریان* | ||
| مرکز تحقیقات نجوم و اخترفیزیک مراغه (ریام)، دانشگاه مراغه، مراغه، ایران. | ||
| چکیده | ||
| تحولات و رفتار پویای خورشید بهعنوان نزدیکترین ستاره به زمین حائز اهمیت است. لکه (لک)های شیدسپهری بهعنوان معیاری از فعالیت خورشید هستند که به مکان تجمعگاههای میدانهای مغناطیسی خورشید نیز شناخته میشوند. دادههای مورداستفاده از ابتدای چرخه 15 تا انتهای چرخه 24 فعالیت خورشیدی از پایگاه دادهای SILSO واقع در رصدخانه سلطنتی بلژیک جمعآوری شدهاند. در این مقاله، ما به بررسی پیچیدگی سری زمانی چرخههای مختلف لکههای خورشیدی در چارچوب آنتروپی و و تحلیل همبستگی بلندبرد آنها مبتنیبر تحلیل محدوده بازمقیاسگذاریشده (تحلیل R/S) میپردازیم. ابتدا، با اعمال رویکرد آنتروپی چندمقیاسه روی سری زمانی چرخههای مختلف لکههای خورشید، این سامانه پیچیده را در ضریبمقیاسهای گوناگون مورد بررسی قرار میدهیم. نتایج سطح زیر نمودار آنتروپیهای چند مقیاسه نشان دادند که میزان این نوع آنتروپی در چرخه 24 و چرخه 20 بهترتیب کمترین و بیشترین مقدار را به خود اختصاص میدهد که بهترتیب نشاندهنده میزان پیچیدگی کمتر و بیشتر این چرخهها است. همچنین، با استفاده از نمای هرست بهدستآمده از اعمال محدوده بازمقیاسگذاریشده روی سری زمانی لکههای چرخهها که همگی در بازه 81/0 تا 86/0 قرار دارند دریافتیم که همه چرخهها دارای همبستگی بلندبرد هستند. ما در این مقاله، نشان دادیم که در بین چرخهها، چرخه 20 دارای بیشترین حافظه بلندبرد در میان دیگر چرخهها است. در نهایت، دریافتیم که با برازش تابع نمایی-توانی بهصورت به میانگین آنتروپیهای چندمقیاسه که هم دارای جمله توانی افزایشی و هم دارای جمله نمایی کاهشی است میتوان پایداری سامانه را بعد از عبور از ضریب مقیاسهای گذار توضیح داد. | ||
| کلیدواژهها | ||
| آنتروپی چندمقیاسه؛ چرخه فعالیت خورشیدی؛ لکههای خورشیدی؛ همبستگی بلندبرد | ||
| عنوان مقاله [English] | ||
| Multiscale entropy analysis and long-range correlation of different sunspot cycles | ||
| نویسندگان [English] | ||
| Mohsen Javaherian | ||
| Research Institute for Astronomy and Astrophysics of Maragha (RIAAM), University of Maragheh, Maragheh, Iran. | ||
| چکیده [English] | ||
| The sunspot system represents a quintessential example of astrophysical complexity, where nonlinear interactions within the solar dynamo generate emergent patterns across multiple spatiotemporal scales. Our investigation of ten solar cycles (15-24) through the lens of multiscale entropy (MSE) analysis reveals profound insights into this complex system. We collected sunspot data (the number of daily sunspots and daily standard deviation of sunspot counting) from the Royal Observatory of Belgium's SILSO database, and decomposed the full time series into individual cycles. Then, we applied MSE approach to time series of individual cycles of sunspots. MSE analysis has two main steps: 1-coarse-graining the time series, and 2-calculating sample entropy for each coarse-grained series. The MSEs of cycles are computed up to scale factor τ=20 and tolerance r=0.15–parameters carefully chosen to capture how information complexity evolves under progressive coarse-graining procedure. This approach is particularly suited to non-stationary systems where traditional entropy measures fail, as it quantifies how structural regularity changes when observed at different temporal resolutions. For studying the long-range correlated behavior of solar cycles, we employed rescaled range (R/S) analysis. It calculates the Hurst exponent (H), which characterizes the system's fractal geometry and persistence (0.5<H<1) or anti-persistence (H<0.5). Using this approach, obtained Hurst exponents ranged in 0.81-0.86 across all different cycles, providing robust mathematical evidence of persistent long-range memory. This persistent behavior which is valid for Hurst exponents ranged in (0.5, 1) signifies fractal organization where correlations follow a power-law decay. Such scaling behavior implies that sunspot dynamics exhibit 1) statistical self-similarity across observational timescales, and 2) information encoding where subsystem behavior reflects global organization. These characteristics align precisely with self-organized criticality (SOC) – a universal mechanism where driven-dissipative systems spontaneously evolve toward critical states characterized by scale-invariant fluctuations. So, within this framework, it is revealed that the different cycles of solar activity have long-term memory in their time series. Since the sunspots have the magnetic origin, we can say that the solar magnetic field operates near a critical threshold, enabling energy release through avalanches of magnetic reconnection events. Furthermore, it was discovered that neither mean daily sunspot number nor mean standard deviation of daily sunspot number have no meaningful relation with MSE-derived complexity. Quantifying complexity through the area under MSE curves revealed Cycle 20 as exhibiting peak complexity; while Cycle 24 showed minimal complexity. Using proposed methods, we found that among all cycles of solar activity with long-range correlated behaviors, Cycles 20 and 24 had the maximum and minimum long-range memory in their time series, respectively. The power-law exponential function as is fitted to ensemble-averaged entropy profiles. The exponential term induces an overall decreasing trend, yet its form creates a dynamic equilibrium between growth and decay up to scale factor τ=20. This functional behavior captures an initial growth phase followed by saturation, potentially indicating system stability. Crucially, it highlights that while complexity evolves differently across scales in various systems, it is universally moderated by a limiting factor. | ||
| کلیدواژهها [English] | ||
| Multiscale entropy, solar activity cycle, sunspots, long-range correlation | ||
| مراجع | ||
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Alipour, N., & Safari, H. (2015). Statistical properties of solar coronal bright points. The Astrophysical Journal, 807(2), 175. Balzter, H., Tate, N. J., Kaduk, J., Harper, D., Page, S., Morrison, R., Muskulus, M., & Jones, P. (2015). Multi-scale entropy analysis as a method for time-series analysis of climate data. Climate, 3(1), 227-240. Booth, C.G., Kaen, F.R., & Koveos, P.E. (1982). R/S analysis of foreign exchange rates under two international monetary regimes. Journal of monetary Economics, 10, 407-415. Chou, C.-M. (2012). Applying multiscale entropy to the complexity analysis of rainfall-runoff relationships. Entropy, 14, 945-957. Clette, F., Svalgaard, L., Vaquero, J.M., & Cliver, E.W. (2014). Revisiting the sunspot number. A 400-year perspective on the solar cycle, Space Science Reviews, 186(1-4), 35-103. Clette, F., & Lefèvre, L. (2015). SILSO sunspot number V2.0. WDC SILSO-Royal Observatory of Belgium (ROB), Institutional Homepage Catalog. Clette, F., & Lefévre, L. (2016). The new sunspot number: assembling all corrections. Solar Physics, 291(9-10), 2629-2651. Clette, F., Lefèvre, L., Cagnotti, M., Cortesi, S., & Bulling, A. (2016 a). The revised Brussels-Locarno sunspot number (1981-2015), Solar Physics, 291 (9-10), 2733-2761. Clette, F., Cliver, E.W., Lefèvre, L, Svalgaard, L., Vaquero, J.M., & Leibacher, J.W. (2016 b). Preface to topical issue: recalibration of the sunspot number. Solar Physics, 291 (9-10), 2479-2486. Costa, M., Goldberger, A.L., & Peng, C.-K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89(6), 068102. Costa, M., Peng, C.-K., L. Goldberger, A., & Hausdorff, J. M. (2003). Multiscale entropy analysis of human gait dynamics. Physica A: Statistical Mechanics and Its Applications, 330(1), 53-60. Costa, M., Goldberger, A.L., & Peng, C.-K. (2005). Multiscale entropy analysis of biological signals. Physical Review E, 71(2), 021906. Costa, M., & Goldberger, A.L. (2015). Generalized multiscale entropy analysis: application to quantifying the complex volatility of human heartbeat time series. Entropy, 17(3), 1197-1203. Daei, F., Safari, H., & Dadashi, N. (2017). Complex network for solar active regions. The Astrophysical Journal, 845(1), 36. Delgado-Bonal, A. & Marshak, A. (2019). Approximate Entropy and Sample Entropy: A comprehensive tutorial. Entropy, 21(6), 541. Feder, J. (2013). Fractals (Physics of Solids and Liquids), Springer US. Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Phys. Rev. A, 33, 1134-1140. Gheibi, A., Safari, H., & Javaherian, M. (2017). The solar flare complex network. The Astrophysical Journal, 847(2), 115. Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Phys. Rev. Lett., 50, 346-349. Hathaway, D.H. (2015). The Solar Cycle. Living Review in Solar Physics, 12, 4. Humeau-Heurtier, A. (2015). The multiscale entropy algorithm and its variants: a review. Entropy, 17(5), 3110-3123. Hurst, H.E. (1951). Long-term storage of reservoirs: an experimental study. Transactions of the American Society of Civil Engineers, 116, 770-799. Hurst, H.E., Black, R.P., & Simaika, Y.M. (1965). Long-term storage: an experimental study. Constable London. Javaherian, M., Safari, H., Dadashi, N., & Aschwanden. M.J. (2017). Statistical properties of photospheric magnetic elements observed by the helioseismic and magnetic imager onboard the solar dynamics observatory. Solar Physics, 292, 164. Javherian, M., & Mollaei, S. (2021). Multiscale entropy analysis of gravitational waves. Advances in High Energy Physics, 2021(6643546), 1-7. Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and Its Applications, 316(1), 87-114. Kantz, H., & Schreiber, T. (2003). Nonlinear Time Series Analysis (2nd ed.). Cambridge: Cambridge University Press. Kononovicius, A. (2020). Physics of risk: Power-law in exponential growth, Vilnius University Faculty of Physics, Institute of Theoretical Physics and Astronomy. https://rf.mokslasplius.lt/power-law-in-exponential-growth/ Livingston, W., Harvey, J. W., Malanushenko, O. V., and Webster, L. (2006). Sunspots with the strongest magnetic fields. Solar Physics, 239, 41-68. Lotfi, N., Javaherian, M., Kaki, B., Darooneh, A. H., & Safari, H. (2020). Ultraviolet solar flare signatures in the framework of complex network. Chaos, 30(4), 043124. Lu, Y., & Wang, J. (2017). Multivariate multiscale entropy of financial markets. Communications in Nonlinear Science and Numerical Simulation, 52, 77-90. Mandelbrot, B.B. (1972). Statistical methodology for non-periodic cycles: From the covariance to R/S analysis. Annals of Economic and Social Measurement, 1, 259–290. Mandelbrot, B.B., & Wallis, J.R. (1969). Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 5, 967-988. Mathew, S. K., Martínez Pillet, V., Solanki, S. K., and Krivova, N. A. (2007). Properties of sunspots in cycle 23. I. Dependence of brightness on sunspot size and cycle phase. Astronomy and Astrophysics, 465, 291-304. Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(6), S2. Mohammadi, Z., Alipour, N., Safari, H., & Zamani, F. (2021). Complex network for solar protons and correlations with flares. Journal of Geophysical Research (Space Physics), 126(7), e28868. Mollaei, S., Darooneh, A.H., & Karimi, S. (2019). Multi-scale entropy analysis and Hurst exponent. Physica A: Statistical Mechanics and its Applications, 528, 121292. Pesnell, W. D. (2012). Solar Cycle Predictions (Invited Review). Solar Physics, 281(1), 507-532. Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297-2301. Priest, E. (2014). Magnetohydrodynamics of the Sun. New York: Cambridge University Press. Qamar, W., Hussain, M., Zaheer, M. B., Akram, J., Sadiq, N., & Uddin, Z. (2025). Prediction of sunspot numbers via Weibull distribution and deep learning. Astrophysics and Space Science, 370(7), 68. Rempel, M. (2011). Penumbral fine structure and driving mechanisms of large-scale flows in simulated sunspots. Astrophysical Journal, 729, 5. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology, 278(6), H2039–H2049. Rodríguez, J.-V., Rodríguez-Rodríguez, I., & Lok Woo, W. (2022). Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series. Publications of the Astronomical Society of the Pacific, 134(1042), 124201. Rypdal, K. (2018). Empirical Growth Models for the Renewable Energy Sector. Advances in Geosciences, 45, 35-44. Solanki, S. K. (2003). Sunspots: an overview. Astronomy and Astrophysics Review, 11, 153-286. Suyal, V., Prasad, A., & Singh, H. P. (2009). Nonlinear time series analysis of sunspot data. Solar Physics, 260(2), 441-449. Tajik, Z., Javaherian, M., Daei, F., Taran, S., Alipour, N., & Safari, H. (2023). Behavior of the solar coronal holes around the maximum activity of the cycle 24. Advances in Space Research, 72(5), 1884-1897. Taran, S., Khodakarami, E., & Safari, H. (2022). Complex network view to solar flare asymmetric activity. Advances in Space Research, 70(8), 2541-2550. Vasconcelos, G.L., Macêdo, A.M.S., Duarte-Filho, G.C. et al. (2021). Power law behaviour in the saturation regime of fatality curves of the COVID-19 pandemic. Scientific Reports, 11, 4619. Wu, S.-D., Wu, C.-W., Lin, S.-G., Wang, C.-C., & Lee, K.-Y. (2013). Time Series Analysis Using Composite Multiscale Entropy. Entropy, 15(3), 1069-1084. Zeng, S., Zhu, S., Huang, Y., Zeng, X., Zheng, S., & Deng, L. (2025). Prediction of solar cycles 26 and 27 based on LSTM-FCN. New Astronomy, 117, 102353. Zolfaghari Nikanjam, S., Khalesifard, H. R., & Abedini, Y. (2017). Estimation of Dus Downfall Time in Dusty Days using the Correlation between PM10 and Sunphotometer Data. Atmospheric Measurement Techniques Discussions, 2017, 1-8. Zurita-Valencia, T. & Muñoz, V. (2023). Characterizing the solar activity using the visibility graph method. Entropy, 25(2), 342. | ||
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