WITHDRAWN The January 1, 2024, Noto Hanto, Japan, Mw 7.6 Earthquake as a Plausible ‘Dragon King’ Event
Description:
WITHDRAWN We query whether the January 1, 2024, Noto Hanto, Japan, MW7.6 earthquake, which punctuated the intense and long-lasting swarm, can be regarded as a ‘Dragon King event’ in the perspective of ‘Dragon King’ theory. We analyzed the earthquake catalogue from JMA since 2004 which uses MV to quantify an earthquake and MW to quantify those events with moment tensor solutions. We inspected the frequency-magnitude distribution and generated the rank-ordering plots, within a fixed spatial range around the Noto Peninsula, to determine whether the MW 7.6 event is an outlier which significantly deviates from the power law scaling. We obtained that for the period from 2004 to 2024 the earthquake cannot be regarded as a significant ‘Dragon King’ event, while for the period from 2021 to 2024 the earthquake can be regarded significantly as a ‘Dragon King’ event. This observation seems consistent with the report that complex precursory behavior has occurred since December 2020, implying the predictability of the ‘Dragon King’ event. We suggest that detailed study of this earthquake, based on the previous results of investigation, may contribute to the theory of the mechanism and prediction of a ‘seismic Dragon King’.
Session: Towards Advancing Earthquake Forecasting and Nowcasting: Recent Progress Using Ai-Enhanced Methods [Poster Session]
Type: Poster
Date: 5/1/2024
Presentation Time: 08:00 AM (local time)
Presenting Author: Yue
Student Presenter: No
Invited Presentation:
Authors
Yue Liu Presenting Author liuyue126liuyue@126.com China Earthquake Administration |
Yongxian Zhang yxzhseis@sina.com China Earthquake Administration |
Zhongliang Wu Corresponding Author wuzl@cea-igp.ac.cn China Earthquake Administration |
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WITHDRAWN The January 1, 2024, Noto Hanto, Japan, Mw 7.6 Earthquake as a Plausible ‘Dragon King’ Event
Category
Towards Advancing Earthquake Forecasting and Nowcasting: Recent Progress Using Ai-Enhanced Methods