Over the past decade, advances in near-fault observation technologies have provided new insights into fault mechanics and earthquake generation, enabled by multidisciplinary data acquisition and integrated observations. The combination of dense, multidisciplinary monitoring networks (e.g. Near-Fault Observatories, large-N arrays, Distributed Acoustic Sensing, low cost GNSS) with advanced processing techniques, including deep learning and automatic detection pipelines, improves the characterization of natural and induced earthquakes with unprecedented detail. This multidisciplinary approach reveals fault structures, stress accumulation processes, rupture initiation and evolution, seismicity and fluid migration, aseismic creep and postseismic deformation.
The geoscience community is converging on interdisciplinary approaches that use these observations to answer key questions about earthquake rupture mechanics and seismic hazard.
This session invites contributions presenting new approaches in automated and machine learning-based seismic monitoring, developments in real-time and end-to-end workflows for earthquake detection and characterization, and modeling of rupture processes using data from dense infrastructures. We invite contributions that use multiparameter observation integration, and discuss innovations in instrumentation, including DAS and geochemical sensors. We also encourage contributions on Earth observation, fault imaging, as well as data integration and software development from near-fault observatories and induced-seismicity episodes.