📝 SummarXiv

Abstract

The dragon-king earthquake hypothesis proposes that some very large to great earthquakes are not merely the extreme end of the frequency-magnitude Gutenberg-Richter distribution (FMD), but are generated by distinct physical mechanisms, making them statistical outliers. We develop a data-driven framework to systematically test the dragon-king earthquake hypothesis. Our method combines objective spatial clustering, based on data-adaptive kernel density estimation (KDE), with a high-power sequential outlier detection technique. For each identified cluster, we exam sine the tail of the FMD to identify anomalous events. Candidate dragon-kings are evaluated via robust statistical tests, primarily the max-robust-sum (MRS) test with inward sequential testing. For each observed statistic of the MRS test, we calculate its p-value defined as the probability that this statistic could be generated by the null distribution. We apply this framework to seismicity surrounding the 1975 Haicheng mL 7.4 and 1976 Tangshan mL 7.9 earthquakes. For Haicheng, the mainshock shows a strong dragon-king signature in its pre-mainshock sequence, with p-values between 0.03 and 0.07 across a stable range of KDE density thresholds used to define natural seismicity clusters. Post-mainshock and combined sequences yield slightly higher p-values (up to 0.09). In contrast, the Tangshan mainshock exhibits a weaker outlier signal before the event (p-values in the range 0.05-0.15) but a stronger dragon-king signature afterward, with p-values from 0.015 to 0.05. The evidence that the Haicheng and Tangshan mainshocks exhibit dragon-king characteristics supports the idea that some large and great earthquakes arise from a maturation process that may enhance predictability. Haicheng provides a clear case study, while retrospective analyses suggest that Tangshan might also have been forecasted under more systematic procedures.