[S-SS12] Statistical seismology and underlying physical processes

[SSS12-P21] Beyond the Gutenberg-Richter law? Testing Dragon-king distributions in seismicity of China

*Jiawei Li¹ (1.Southern University of Science and Technology)

Keywords:Seismicity, Gutenberg-Richter law?, Dragon-king event

A comprehensive quantitative analysis of major earthquake mechanisms can enhance our understanding of fault ruptures and seismicity patterns. This research has the potential to improve earthquake predictability and disaster mitigation strategies. In 2009, one of us introduced the dragon-king theory, a framework to identify and analyze extreme outliers, or dragon-king events, and to manage their risks (Sornette, 2009; Pisarenko & Sornette, 2012; Sornette & Ouillon, 2012; de S. Cavalcante et al., 2013; Wheatley & Sornette, 2015). Here, we explore the application of this theory to seismology, proposing a framework to identify dragon-king earthquake events. Our framework combines completeness magnitude analysis and the Anderson-Darling distance to seismicity into incomplete, complete (power law/Gutenberg-Richter), and potentially non-power law segments. Using sequential testing methods and the max-robust-sum (MRS) test statistic, we identify true dragon-king events and quantify their significance with a block test. Applying this framework to data from China, we test for dragon-king earthquakes deviating from the Gutenberg-Richter distribution. Despite practical challenges, this approach aims to refine statistical seismology by reevaluating earthquake rupture classifications, enhancing analytical tools and research methodologies.

References
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Sornette, D. (2009). Dragon-Kings, black Swans and the Prediction of Crises. Int. J. Ter. Sci. Eng., 2(1), 1-18
Sornette, D., & Ouillon, G. (2012). Dragon-kings: mechanisms, statistical methods and empirical evidence. Eur. Phys. J. Special Topics, 205(1), 1-26.
Pisarenko, V. F. and Sornette, D. (2012). Robust Statistical Tests of Dragon-Kings beyond Power Law Distributions, Eur. Phys. J. Special Topics 205, 95-115.
Wheatley, S., & Sornette, D. (2015). Multiple outlier detection in samples with exponential & Pareto tails: Redeeming the inward approach & detecting dragon kings. Swiss Finance Institute Research Paper,(15-28) (http://arxiv.org/abs/1507.08689 and http://ssrn.com/abstract=2645709)