2023年5月21日(日) 10:45 〜 12:00 104 (幕張メッセ国際会議場)

コンビーナ:服部 克巳(千葉大学大学院理学研究科)、劉 正彦(国立中央大学太空科学研究所)、Ouzounov Dimitar(Center of Excellence in Earth Systems Modeling & Observations (CEESMO) , Schmid College of Science & Technology Chapman University, Orange, California, USA)、Qinghua Huang(Peking University)、座長:Ouzounov Dimitar(Center of Excellence in Earth Systems Modeling & Observations (CEESMO) , Schmid College of Science & Technology Chapman University, Orange, California, USA)、劉 正彦(国立中央大学太空科学研究所)

We have acquired a lot of knowledge on precursors and earthquake
preparation. This session expands the interdisciplinary discussions on
the preparation process of earthquake and earthquake predictability by
presenting the latest progress in studying the physically based
Pre-earthquake phenomena. New observations from space and ground have
provided evidence that may enhance better the understanding of the tectonic
activity. The session anticipates talks that include but are not limited to
observations and analyses of seismic, electrical, electromagnetic,
electro-chemical, and thermodynamic processes related to stress changes
in the lithosphere, along with their statistical and physical validation.
Presentations on the latest observational results associated with major
earthquakes obtained by different methodologies are welcomed. The topics
of the session are as follows but not limited.

-General discussion on the earthquake preparation process and the physics of
pre-earthquake signals
- Theory, modeling, laboratory experiments, and computational simulation for
generation and propagation of pre-earthquake signals and their
connection with the seismic cycle
- Multi-parameter observations, detection, and validation of
pre-earthquake signals
- Cross-disciplinary studies, practical and technical approaches for
a better understanding of earthquake preparation processes and their
connection with seismicity.
- Applications of multi-parameter Machine Learning and AI approaches
for pre-earthquake signals identification, and data assimilation for
practical forecast model.