Multidisciplinary and Interdisciplinary (M) | ||||
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Session Sub-category | Technology & Techniques (TT) | |||
Session ID | M-TT36 | |||
Session Title | Recent advances in forecasting/nowcasting and seismic hazard reduction | |||
Short Title | Advances in forecasting/nowcasting | |||
Date & Time | Oral Session |
AM1 Sun, 26 MAY | ||
On-site Poster Coretime |
PM3 Sun, 26 MAY | |||
Main Convener | Name | Yangkang Chen | ||
Affiliation | University of Texas at Austin | |||
Co-Convener 1 | Name | John B Rundle | ||
Affiliation | University of California Davis | |||
Co-Convener 2 | Name | Dimitar Ouzounov | ||
Affiliation | Center of Excellence in Earth Systems Modeling & Observations (CEESMO) , Schmid College of Science & Technology Chapman University, Orange, California, USA | |||
Co-Convener 3 | Name | Katsumi Hattori | ||
Affiliation | Department of Earth Sciences, Graduate School of Science, Chiba University | |||
Session Language | E | |||
Scope (Session Description) |
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. |
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Presentation Format | Oral and Poster | |||
Collaboration | Joint with | AGU | ||
Co-sponsoring Society |
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