In recent decades, observational seismology has advanced rapidly, driven by expanding computational capabilities and the growing availability of data. Emerging approaches such as Distributed Acoustic Sensing (DAS) and Large-N nodal arrays introduce exciting opportunities to go beyond standard catalog building for subsurface investigation and analysis, as well as present new challenges. The integration of large datasets, advanced monitoring tools, and innovative processing techniques has paved the way for new discoveries. Machine learning-based methods, for example, can detect more earthquakes than traditional techniques, enabling the identification of smaller events and revealing previously hidden patterns. Likewise, fully data-driven and waveform-based methods are enhancing our ability to image the Earth's crust with increasing resolution. However, automated approaches can introduce errors or biases if uncertainties are not carefully assessed. Uncertainty quantification therefore remains a central challenge, essential for ensuring robust and reliable scientific outcomes.
This session invites contributions presenting new approaches to the analysis of large seismic datasets, whether in offline playback or (near) real-time applications, across a wide range of tectonic settings and spatial scales. We particularly welcome methods that integrate rigorous error and uncertainty analysis.
Submissions may focus on classical aspects of seismicity analysis, such as event detection, location, magnitude estimation, and source characterization, as well as novel instrumental or theoretical developments. We encourage contributions spanning diverse applications, including automated observatory workflows, enhanced geothermal systems (EGS), carbon capture and storage (CCS) monitoring, and studies from laboratory to regional scales.
Marine Denolle