• ISSN 2097-1893
  • CN 10-1855/P

地震统计物理研究的新视角:“龙王”理论及“龙王”地震事件

New horizon in the statistical physics of earthquakes: Dragon-king theory and dragon-king earthquakes

  • 摘要: 系统地定量考察大地震发震机理是否特殊,不仅能够深化对断层破裂和地震活动规律的理解,而且有望促进大地震可预测性研究和提升防灾减灾策略的有效性. 本文作者之一于2009年提出的“龙王”理论,旨在为那些内源性极端离群值(即“龙王”事件)提供一个量化的识别和检验框架. 该理论为解释、预测和管控这些罕见但影响巨大的事件提供了非常有价值的分析工具. 本文讨论了将其应用于地震学的可行性,提出用古登堡-里克特定律的离群值作为识别“龙王”地震事件的标志,并结合断层耦合强度、有限断层破裂、破裂几何复杂性及地震动力学数值模拟实验呈现的失稳破裂探讨了可能导致这些特殊事件产生的四个地震学机制. 尽管“龙王”理论在地震学的具体应用还面临诸多实际挑战,但仍有望显著丰富统计地震学的研究内容. 通过从统计物理研究的视角重新系统性审视地震破裂类型的分类,并将这些认识与其背后的物理机制相结合,本文呈现的内容将可以极大提升地震统计物理研究领域的分析手段和研究深度.

     

    Abstract: A systematic quantitative investigation into whether the mechanisms of large earthquakes are unique could significantly deepen our understanding of fault rupture and seismicity patterns. This research holds the potential to advance our ability to predict large earthquakes and enhance the effectiveness of disaster prevention and mitigation strategies. In 2009, one of us introduced the dragon-king theory, offering a quantitative framework for identifying and testing extreme outliers—referred to as dragon-king events—that are endogenously generated. This theory provides valuable tools for explaining, predicting, and managing the risks associated with these rare but highly impactful events. The present paper discusses the feasibility of applying this theory to seismology, proposing that dragon-king earthquake events can be identified as outliers to the Gutenberg-Richter law. It also examines four seismological mechanisms that may contribute to the occurrence of these extraordinary events, considering factors such as fault coupling intensity, finite fault rupture, rupture geometric complexity, and run-away unstable rupture revealed by numerical simulations of earthquake dynamics. Although applying the dragon-king theory to seismology presents practical challenges, it offers the potential to significantly enrich statistical seismology. By reexamining the classification of earthquake rupture types through a statistical physics lens and integrating these insights with underlying physical mechanisms, this approach can greatly enhance the analytical tools and depth of research in the field of statistical seismology.

     

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