コンビーナ:Francesco Grigoli(ETH Zurich Swiss Federal Institute of Technology Zurich)、Bogdan Enescu(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)、加藤 愛太郎(東京大学地震研究所)、青木 陽介(東京大学地震研究所)

In the last two decades the number of high quality seismic instruments being installed around the world has grown exponentially and likely will continue to grow in the coming decades. This led to a dramatic increase in the volume of available seismic data and pointed out the limits of the current standard routine seismic analysis, often performed manually by seismologists. Exploiting this massive amount of data is a challenge that can be overcome by using new generation, fully automated and noise-robust seismic processing techniques. In the last years, waveform-based detection and location methods have grown in popularity and their application has dramatically improved seismic monitoring capability. Moreover, machine learning techniques, which are dedicated methods for data-intensive applications, are showing promising results in seismicity characterization applications, opening new horizons for the development of innovative, fully automated and noise-robust seismic analysis methods. Such techniques are particularly useful when working with data sets characterized by large numbers of weak events, with low signal-to-noise ratio, such as those collected in induced seismicity, seismic swarms and volcanic monitoring operations. This session aims to bring to light new methods that can be applied to large data sets, either retro-actively or in (near) real-time, to characterize seismicity (i.e., perform detection, location, magnitude and source mechanisms estimation) at different scales and in different environments. We thus encourage contributions that demonstrate how the proposed methods help improve our understanding of earthquake and/or volcanic processes.