E

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.