固体地球科学(S) | |||
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セッション小記号 | 地震学(SS) | ||
セッションID | S-SS05 | ||
タイトル | 和文 | Innovative data analysis methods for characterization of seismicity | |
英文 | Innovative data analysis methods for characterization of seismicity | ||
タイトル短縮名 | 和文 | Innovative seismicity analysis methods | |
英文 | Innovative seismicity analysis methods | ||
代表コンビーナ | 氏名 | 和文 | Francesco Grigoli |
英文 | Francesco Grigoli | ||
所属 | 和文 | ETH Zurich Swiss Federal Institute of Technology Zurich | |
英文 | ETH Zurich Swiss Federal Institute of Technology Zurich | ||
共同コンビーナ 1 | 氏名 | 和文 | Enescu Bogdan |
英文 | Bogdan Enescu | ||
所属 | 和文 | 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 | |
英文 | Department of Geophysics, Kyoto University | ||
共同コンビーナ 2 | 氏名 | 和文 | 加藤 愛太郎 |
英文 | Aitaro Kato | ||
所属 | 和文 | 東京大学地震研究所 | |
英文 | Earthquake Research Institute, the University of Tokyo | ||
共同コンビーナ 3 | 氏名 | 和文 | 青木 陽介 |
英文 | Yosuke Aoki | ||
所属 | 和文 | 東京大学地震研究所 | |
英文 | Earthquake Research Institute, University of Tokyo | ||
発表言語 | E | ||
スコープ | 和文 | 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. | |
英文 | 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. | ||
発表方法 | 口頭および(または)ポスターセッション |