Predictive understanding of the dynamics of seismicity is one of the greatest challenges of modern geophysics. To accomplish such an understanding requires a combination of exploratory data analysis and relevant mathematical modeling. This project will provide a framework for modeling and analyzing regional seismicity based on the stochastic quantization approach and the use of non-linear filtering for marked point processes. The resulting description will allow one to represent regional fault networks, including details such as complex geometry, memory, interactions, and structural heterogeneities. The proposed research will expand upon ideas captured in existing geophysical models. In particular, one of the most important goals is to enhance Epidemic Type Aftershock Sequence (ETAS) approach to short-term earthquake modeling and forecasts and improve the model performance on larger spatial and temporal scales by incorporating known aspects of regional fault systems. The models constructed in the project will be tested on the observed seismicity of southern California and will leverage high-quality data from Southern California Earthquake Data Center (SCEDC) and the Southern California Earthquake Center Community Fault Model (SCEC CFM).
Natural hazards pose a threat to society, and earthquakes are probably the greatest danger to the built environment in tectonically active areas. This project will contribute to the predictive understanding of the seismicity dynamics by merging the state-of-the-art mathematical methods and seismicity data. Ultimately, the proposed work will be used to formulate earthquake forecasting strategies, which will be tested within the international Collaboratory for the Study of Earthquake Predictability (CSEP), recently organized by SCEC. The collaborative and cross-disciplinary approach of this project makes it an ideal training ground for graduate students and young scientists.
