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Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 1

Entropy method for earthquake volatility

Ayşe Metin KARAKAŞ
Sinan ÇALIK
Pages
329–348
Publication Date
2020-03-27
DOI
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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.
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Key Findings & Data:The application of entropy in earthquake can be regarded as the extension of information entropy and probability theory.
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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.

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Cite This Article

KARAKAŞ, A. M., ÇALIK, S. (2020). Entropy method for earthquake volatility. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 1, 329–348. https://ajindex.com/articles/419307

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References (12)

  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.
  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.
  4. 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.
  5. Pincus, S. (2008) Approximate entropy as an irregularity measure for financial data, Econometric Reviews, 27, 4-6, 329-362.
  6. Ubriaco, M. R. (2009) Entropies based on fractional calculus, Physics Letters A, 373, 30, 2516-2519.
  7. Akpinar, S., Akpinar, E. K. (2009) Estimation of wind energy potential using finite mixture distribution models, Energy Conversion and Management, 50, 4, 877-884.
  8. 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.
  9. Moreno, B., García-Álvarez, M. T. (2011) Analyzing the effect of Renewable Energy Sources on Electricity Prices in Spain, A Maximum Entropy Econometric Approach.
  10. Wang, G. J., Xie, C., Han, F. (2012) Multi-scale approximate entropy analysis of foreign exchange markets efficiency, Systems Engineering Procedia, 3, 201-208.
  11. 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
  12. 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.2