In recent decades, observational seismology has advanced rapidly due to expanding computational capabilities and the increasing volume of seismic data. In addition to the standard seismicity dataset, new data obtained through methods such as Distributed Acoustic Sensing (DAS) or Large-N nodal arrays present new challenges for the seismological community while opening up numerous applications and increasing the potential for subsurface investigation and analysis.
The combination of big datasets, advanced monitoring instruments, and innovative processing techniques is driving breakthroughs in various seismology fields. Machine learning-based methods for seismic data analysis can now detect more earthquakes than traditional methods, greatly improving the detection of smaller earthquakes and revealing previously hidden patterns in earthquake behavior. Additionally, full-data-driven and waveform-based methods have enhanced our ability to image the Earth's crust with high resolution.
However, automated processing approaches can introduce errors or biases if uncertainties are not carefully quantified, emphasizing the need for uncertainty assessment as a crucial area of future research. This session aims to promote new methods for analyzing large datasets either in offline playback mode or in (near) real-time, to study seismic activity on different length scales and in various tectonic environments, and to encourage methods for more robust error-uncertainties analysis, therefore leading to a more solid evaluation of research outcomes.
We encourage contributions not only focusing on classical seismicity analysis techniques such as event detection, location, magnitude, and source-mechanism estimation but also encourage submissions on innovative instrumental and theoretical applications. Finally, the contributions can cover a broad spectrum of topics, including automated seismic observatory procedures, geothermal exploitation, EGS and CSS monitoring, and studies ranging from laboratory to regional scales.