Recent advances in physical and statistical modelling based on seismicity patterns provide new insights into the preparation of large earthquakes and the temporal, spatial, and magnitude evolution of seismicity.
Improvements in monitoring technologies now deliver seismic data of unprecedented quality and quantity. Earthquake catalogues are more complete and accurate than ever, and many are now publicly available, enabling analysing understudied regions and expanding global knowledge. New-generation catalogues, sometimes compiled with machine learning, reveal seismicity structures in ways not previously possible.
Additionaly, geodetic, geological, and geochemical data, fluid analyses, laboratory experiments, and earthquake simulators generating synthetic catalogues help refine models and test hypotheses. Integrating such multidisciplinary perspectives enhances our understanding of earthquake generation.
To exploit these datasets, statistical approaches and machine learning are essential. These tools uncover hidden relationships and clustering, and address challenges of data inhomogeneity, paving the way for deeper understanding and robust forecasting.
We invite contributions on developments in physical and statistical modelling and machine learning, including:
• Spatial, temporal, and magnitude properties of earthquake statistics
• Earthquake clustering analyses
• Effects of fluid diffusion and geodetic deformation on seismicity
• Physical and statistical models, including for understudied regions (e.g., Africa, Southeast Asia)
• Quantitative testing of models
• Data requirements and analyses for validation
• Machine learning applied to seismic data
• Uncertainty quantification in pattern recognition and machine learning
• Reliability and completeness of catalogues
• Time-dependent hazard assessment
• Software and methods for earthquake forecasting