Recent advances in earthquake detection, including machine learning, template matching, DAS, and dense seismic network observations, are rapidly expanding earthquake catalogs. However, translating these developments into reliable seismic hazard and risk assessments requires equally robust seismological characterization, including magnitude estimation, phase picking, velocity models, focal mechanisms, catalog validation, uncertainty quantification, and source parameter measurements. This session focuses on the pathway from earthquake detection to seismic hazard, demonstrating how improved catalog development and event characterization can strengthen hazard models, forecasting, and risk analyses, while also providing insight into earthquake rupture processes.

We welcome contributions on detection methods, catalog and magnitude methodologies, relocation and velocity model improvements, catalog comparisons, uncertainty assessment, and applications of improved earthquake datasets to hazard and risk problems. Studies covering tectonic, induced, or volcanic seismicity, and those integrating new computational tools or reassessing existing tools and catalogs, are especially encouraged.