2024年5月26日(日) 09:00 〜 10:15 303 (幕張メッセ国際会議場)

コンビーナ:Grigoli Francesco(University of Pisa)、Enescu Bogdan(京都大学 大学院 理学研究科 地球惑星科学専攻 地球物理学教室)、青木 陽介(東京大学地震研究所)、内出 崇彦(産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、座長:Enescu Bogdan(京都大学 大学院 理学研究科 地球惑星科学専攻 地球物理学教室)、内出 崇彦(産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、青木 陽介(東京大学地震研究所)、Francesco Grigoli(University of Pisa)

In the last two decades, the number of high-quality seismic instruments installed worldwide has grown exponentially and likely will continue to grow in the coming decades, producing larger and larger datasets. This dramatic increase in the volume of available seismic data is partially due to the rising popularity of new technologies for seismic data acquisition based on fiber optics, characterized by an extremely high spatial and temporal sampling. Such systems are making seismological datasets grow in size and variety at an exceptionally fast rate, pushing the limit of current data analysis techniques. This data explosion, combined with new data analysis paradigms, is opening new research horizons in seismology and related fields. Exploiting the massive amount of data is a challenge that can be overcome by adopting new approaches for seismic data analysis that can lead to enhanced seismic catalogs that can be used in conjunction with advanced statistical or physics-based methods to forecast seismicity or to correlate the seismic activity with other geophysical processes, including stress changes and migration of fluids in the crust or aseismic processes. This session aims to bring to light new methods for the analysis (either offline or in real-time) and quantitative interpretation of seismicity datasets collected across different scales and environments or with new seismic data acquisition technologies, such as fiber-optics-based sensors. Relevant topics to be presented include but are not limited to methods for seismicity characterization, statistical analysis of seismicity patterns in the space-time-magnitude domain, modeling and forecasting of seismicity, and case studies. We thus encourage contributions that demonstrate how the proposed methods or the analysis of large datasets help to improve our understanding of earthquake and/or volcanic processes.