領域外・複数領域(M)
セッション小記号 計測技術・研究手法(TT)
セッションID M-TT36
タイトル 和文 Recent advances in forecasting/nowcasting and seismic hazard reduction
英文 Recent advances in forecasting/nowcasting and seismic hazard reduction
タイトル短縮名 和文 Advances in forecasting/nowcasting
英文 Advances in forecasting/nowcasting
代表コンビーナ 氏名 和文 Yangkang Chen
英文 Yangkang Chen
所属 和文 University of Texas at Austin
英文 University of Texas at Austin
共同コンビーナ 1 氏名 和文 John B Rundle
英文 John B Rundle
所属 和文 University of California Davis
英文 University of California Davis
共同コンビーナ 2 氏名 和文 Ouzounov Dimitar
英文 Dimitar Ouzounov
所属 和文 Center of Excellence in Earth Systems Modeling & Observations (CEESMO) , Schmid College of Science & Technology Chapman University, Orange, California, USA
英文 Chapman University
共同コンビーナ 3 氏名 和文 服部 克巳
英文 Katsumi Hattori
所属 和文 千葉大学大学院理学研究院
英文 Department of Earth Sciences, Graduate School of Science, Chiba University
発表言語 E
スコープ 和文 
Advanced data analysis technologies including machine learning (ML) and artificial intelligence (AI) have brought significant progress in integrating different observations (e.g., acoustic, elastic, remote sensing), as well as understanding the spatial-temporal correlations of earthquakes, which makes the previously intractable forecasting and nowcasting more possible. In addition, new sensing technologies enable the observation and exploration of earthquake precursor signals in an unprecedently high spatio-temporal resolution. These new signal processing techniques and high-quality and high-resolution datasets pave the way for multi-disciplinary studies of earthquake nucleation mechanisms and predictions, as well as advances in seismic hazard reduction. In this session, we welcome the contribution from a wide range of advances in the field of earthquake forecasting and nowcasting including but not limited to machine learning and AI-enhanced methods to integrate multi-purpose datasets;  advances in understanding earthquake nucleation from laboratory to field; benchmark real case studies; new sensing and processing technologies for capturing the pre-earthquake signals; insights of earthquake hazard reduction in the era of predictable earthquakes.
英文 
Advanced data analysis technologies including machine learning (ML) and artificial intelligence (AI) have brought significant progress in integrating different observations (e.g., acoustic, elastic, remote sensing), as well as understanding the spatial-temporal correlations of earthquakes, which makes the previously intractable forecasting and nowcasting more possible. In addition, new sensing technologies enable the observation and exploration of earthquake precursor signals in an unprecedently high spatio-temporal resolution. These new signal processing techniques and high-quality and high-resolution datasets pave the way for multi-disciplinary studies of earthquake nucleation mechanisms and predictions, as well as advances in seismic hazard reduction. In this session, we welcome the contribution from a wide range of advances in the field of earthquake forecasting and nowcasting including but not limited to machine learning and AI-enhanced methods to integrate multi-purpose datasets;  advances in understanding earthquake nucleation from laboratory to field; benchmark real case studies; new sensing and processing technologies for capturing the pre-earthquake signals; insights of earthquake hazard reduction in the era of predictable earthquakes.
発表方法 口頭および（または）ポスターセッション
ジョイントセッション AGU