In the last two decades, the number of high-quality seismic instruments installed worldwide has grown exponentially and likely will continue to grow in the coming decades, producing larger and larger datasets. This dramatic increase in the volume of available seismic data is partially due to the rising popularity of new technologies for seismic data acquisition based on fiber optics, characterized by an extremely high spatial and temporal sampling. Such systems are making seismological datasets grow in size and variety at an exceptionally fast rate, pushing the limit of current data analysis techniques. This data explosion, combined with new data analysis paradigms, including AI-based methods, is opening new research horizons in Seismology and related fields. Exploiting the massive amount of data is a challenge that can be overcome by adopting new approaches for seismic data analysis that can lead to enhanced seismic catalogs that can be used in conjunction with advanced statistical and physics-based methods to forecast seismicity or to correlate the seismic activity with other geophysical processes, including stress changes, migration of fluids in the crust or slow-slip. This session aims to bring to light new methods for the analysis - either offline or in real-time - and quantitative interpretation of earthquake datasets collected across different scales and environments or with new seismic data acquisition technologies, such as fiber-optics-based sensors. Relevant topics include but are not limited to methods for seismicity acquisition and characterization, statistical analysis of seismicity patterns and their relationship with aseismic processes, modeling and forecasting of seismicity, earthquake triggering and case studies. We thus encourage contributions that demonstrate how the proposed methods or the analysis of large datasets help to improve our understanding of earthquake and/or volcanic processes.

In the last two decades, the number of high-quality seismic instruments installed worldwide has grown exponentially and likely will continue to grow in the coming decades, producing larger and larger datasets. This dramatic increase in the volume of available seismic data is partially due to the rising popularity of new technologies for seismic data acquisition based on fiber optics, characterized by an extremely high spatial and temporal sampling. Such systems are making seismological datasets grow in size and variety at an exceptionally fast rate, pushing the limit of current data analysis techniques. This data explosion, combined with new data analysis paradigms, including AI-based methods, is opening new research horizons in Seismology and related fields. Exploiting the massive amount of data is a challenge that can be overcome by adopting new approaches for seismic data analysis that can lead to enhanced seismic catalogs that can be used in conjunction with advanced statistical and physics-based methods to forecast seismicity or to correlate the seismic activity with other geophysical processes, including stress changes, migration of fluids in the crust or slow-slip. This session aims to bring to light new methods for the analysis - either offline or in real-time - and quantitative interpretation of earthquake datasets collected across different scales and environments or with new seismic data acquisition technologies, such as fiber-optics-based sensors. Relevant topics include but are not limited to methods for seismicity acquisition and characterization, statistical analysis of seismicity patterns and their relationship with aseismic processes, modeling and forecasting of seismicity, earthquake triggering and case studies. We thus encourage contributions that demonstrate how the proposed methods or the analysis of large datasets help to improve our understanding of earthquake and/or volcanic processes.