SavedAdd your journal

Journals / Sigma Journal of Engineering and Natural Sciences / 2026 / Volume: 38 Issue: 1

Entropy method for earthquake volatility

Ayşe Metin Karakaş(BITLIS EREN UNIVERSITY)0000-0003-3552-0105
Sinan Çalık(FIRAT UNIVERSITY)0000-0002-4258-1662
Pages
—
Publication Date
2020-03-27
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings indicate the necessity of more detailed studies for a more comprehensive understanding the nature of Earthquake.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, we obtained the volatility of 1. and 2. degree earthquake zones on the same fault line by using entropy method.
📊
Key Findings & Data:The application of entropy in earthquake can be regarded as the extension of information entropy and probability theory.
💡
Conclusion & Impact:These findings indicate the necessity of more detailed studies for a more comprehensive understanding the nature of Earthquake.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Renyi entropy · the shannon entropy · tsallis entropy

Abstract

In this study, we obtained the volatility of 1. and 2. degree earthquake zones on the same fault line by using entropy method. The application of entropy in earthquake can be regarded as the extension of information entropy and probability theory. The entropy theory applied to derive the most likely univariate distributions subject to specified restriction by applying the principle of maximum entropy. These findings indicate the necessity of more detailed studies for a more comprehensive understanding the nature of Earthquake.

🔖

Cite This Article

Karakaş, A. M., Çalık, S. (2020). Entropy method for earthquake volatility. Sigma Journal of Engineering and Natural Sciences, Volume: 38 Issue: 1. https://ajindex.com/articles/1632662

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (27)

  1. Laidler, K.J., (1995) Thermodynamics, In The World of Physical Chemistry, Oxford University Press, New York, NY, USA, 156–240.
  2. Tsallis, C. (1988) Possible generalization of Boltzmann-Gibbs statistics, Journal of Statistical Physics, 52, 479-487. https://link.springer.com/content/pdf/10.1007%2FBF01016429.pdf
  3. Rao, M.,Chen, Y.,Vemuri, B.C., Wang, F. (2004) Cumulative residual entropy: a new measure of information, IEEE transactions on Information Theory, 50 6, 1220-1228. DOI: 10.1109/TIT.2004.828057
  4. Shafee, F. (2007) Lambert function and a new non-extensive form of entropy, IMA journal of applied mathematics, 72, 6, 785-800. https://doi.org/10.1093/imamat/hxm039
  5. Akpinar, S., Akpinar, E. K. (2006) Wind energy analysis based on maximum entropy principle (MEP)-type distribution function, Energy Conversion and Management, 48(4),1140-1149. https://doi.org/10.1016/j.enconman.2006.10.004.
  6. Pincus, S. (2008) Approximate entropy as an irregularity measure for financial data, Econometric Reviews, 27, 4-6, 329-362. https://doi.org/10.1080/07474930801959750
  7. Ubriaco, M. R. (2009) Entropies based on fractional calculus, Physics Letters A, 373, 30, 2516-2519. https://doi.org/10.1016/j.physleta.2009.05.026
  8. Akpinar, S., Akpinar, E. K. (2009) Estimation of wind energy potential using finite mixture distribution models, Energy Conversion and Management, 50, 4, 877-884. https://doi.org/10.1016/j.enconman.2009.01.007
  9. Rompolis, L. S. (2010) Retrieving risk neutral densities from European option prices based on the principle of maximum entropy, Journal of Empirical Finance, 17, 5, 918-937. https://doi.org/10.1016/j.jempfin.2010.04.007
  10. Moreno, B., García-Álvarez, M. T. (2011) Analyzing the effect of Renewable Energy Sources on Electricity Prices in Spain, A Maximum Entropy Econometric Approach. https://hrcak.srce.hr/107272
  11. Wang, G. J., Xie, C., Han, F. (2012) Multi-scale approximate entropy analysis of foreign exchange markets efficiency, Systems Engineering Procedia, 3, 201-208. https://doi.org/10.1016/j.sepro.2011.10.030
  12. Moreno, B., Garcia-Alvarez, M. T. (2013) The role of renewable energy sources on electricity prices in Spain. A maximum entropy econometric model, Strojarstvo: časopis za teoriju i praksu u strojarstvu, 55, 2, 149-159 https://hrcak.srce.hr/107272
  13. Lucia, U. (2013) Entropy and exergy in irreversible renewable energy systems, Renewable and Sustainable Energy Reviews, 20, 559-564. https://doi.org/10.1016/j.rser.2012.12.017
  14. Ormos, M., Zibriczky, D. (2014) Entropy-based financial asset pricing, PloS one, 9, 12, e115742. https://doi.org/10.1371/journal.pone.0115742
  15. Van Erven, T., Harremos, P. (2014) Rényi divergence and Kullback-Leibler divergence, IEEE Transactions on Information Theory, 60, 7, 3797-3820. https://10.1109/TIT.2014.2320500
  16. Azad, A. K., Rasul, M. G., Alam, M. M., Uddin, S. A., Mondal, S. K. (2014) Analysis of wind energy conversion system using Weibull distribution Procedia Engineering, 90,725-732 https://doi.org/10.1016/j.proeng.2014.11.803
  17. Niu, H., Wang, J. (2015) Quantifying complexity of financial short-term time series by composite multiscale entropy measure Communications in Nonlinear Science and Numerical Simulation, 22, 1-3, 375-382. https://doi.org/10.1016/j.cnsns.2014.08.038
  18. Dedu, S., Toma, A. (2015) An Integrated Risk Measure and Information Theory Approach for Modeling Financial Data and Solving Decision Making Problems, Procedia Economics and Finance, 22, 531-537. https://doi.org/10.1016/S2212-5671(15)00252-X
  19. Sati, M. M., Gupta, N. (2015) Some characterization results on dynamic cumulative residual Tsallis entropy, Journal of Probability and Statistics. http://dx.doi.org/10.1155/2015/694203
  20. Sheraz, M., Dedu, S., Preda, V. (2015) Entropy measures for assessing volatile markets. Procedia Economics and Finance, 22, 655-662. https://doi.org/10.1016/S2212-5671(15)00279-8
  21. Stosic, D., Stosic, D., Ludermir, T., Oliveira, W., Stosic, T., (2016) Foreign exchange rate entropy evolution during financial crises, Physica A: Statistical Mechanics and its Applications, 449,233-239. https://doi.org/10.1016/j.physa.2015.12.124
  22. Ram, S. K., Kulia, G., Molinas, M., (2016) On wind Turbine failure detection from measurements of phase currents: a permutation entropy approach, arXiv preprint arXiv:1601.05387. https://arxiv.org/pdf/1601.05387.pdf
  23. Shoaib, M., Siddiqui, I., Rehman, S., Rehman, S. U., Khan, S., Lashin, A. (2016) Comparison of wind energy generation using the maximum entropy principle and the Weibull distribution function. Energies, 9,10, 842 https://doi.org/10.3390/en9100842
  24. Ponta, L., Carbone, A. (2018) Information measure for financial time series: quantifying short-term market heterogeneity, Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2018.06.085
  25. Khammar, A. H., Jahanshahi, S. M. A. (2018) On weighted cumulative residual Tsallis entropy and its dynamic version, Physica A: Statistical Mechanics and its Applications, 491, 678-692 https://doi.org/10.1016/j.physa.2017.09.079
  26. Main, I. G., Al‐Kindy, F. H. (2002). Entropy, energy, and proximity to criticality in global earthquake populations, Geophysical Research Letters, 29, 7, 1-25. https://doi.org/10.1029/2001GL014078
  27. Rao, M.,Chen, Y.,Vemuri, B.C., Wang, F. (2004) Cumulative residual entropy: a new measure of information, IEEE transactions on Information Theory, 50 6, 1220-1228. DOI: 10.1109/TIT.2004.828057