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Room: Exhibit Hall

Date: 5/1/2024

Session Time: 8:00 AM to 5:45 PM (local time)

New technologies like advanced machine learning (ML) of big data (BD) and artificial intelligence (AI), together with signal processing tools that emerged in the past decade, have brought a wave of intensified studies of earthquake forecasting and nowcasting. In addition, fast-expanding datasets due to the installation of dense sensing networks, diversified observations (e.g., acoustic, elastic, satellite observations), injection-induced seismicity from around the world, and high-resolution ML-based catalogs provide more resources and constraints for studying the earthquake nucleation mechanism. These methods also allow the exploration of physical earthquake precursors and call for advanced computing architectures and data management plans in their effective usage. These new methods and datasets open the door to multi-disciplinary collaboration in a seamless way. In this session, we welcome the contribution from a wide spectrum of advances in the field of earthquake forecasting and nowcasting, including but not limited to: new data-driven or physics- based ways for forecasting/nowcasting earthquakes; machine learning and AI-enhanced methods to boost accuracy, verification and reliability; earthquake forecasting/nowcasting from laboratory to field; break-through real case studies; cross-disciplinary studies of earthquake forecasting/nowcasting; and new sensing and processing technologies for capturing the precursor signals. 

Conveners:
Yangkang Chen, University of Texas at Austin (yangkang.chen@beg.utexas.edu)
Katsumi Hattori, Chiba University (khattori@faculty.chiba-u.jp)
Lisa G. Ludwig, University of California, Irvine (lgrant@uci.edu)
Dimitar Ouzounov, Chapman University (ouzounov@chapman.edu)
John Rundle, University of California, Davis (john.b.rundle@gmail.com)

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