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

TL;DR

This paper explores the application of dragon-king theory to seismology, aiming to identify and understand extreme earthquake events as outliers, which could improve earthquake prediction and risk management.

Contribution

It proposes a novel approach to classify and analyze extraordinary earthquakes as dragon-kings, integrating statistical testing with physical mechanisms in seismology.

Findings

Dragon-king events can be identified as outliers to the Gutenberg-Richter law.

Applying dragon-king theory offers new insights into earthquake mechanisms.

Potential to enhance earthquake prediction and risk mitigation strategies.

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 several seismological mechanisms that may contribute to the occurrence of these extraordinary events. 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 testing 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.