Pattern Recognition Applied to Block-and-Sandpaper Model of Seismicity

We are currently developing a predictive algorithm for the block-and-sand paper seismicity model using methodology created by Dr. Keilis-Borok and his associates to foretell critical events in chaotic systems. The model was constructed in the Modeling and Educational Demonstrations Laboratory (MEDL) at the University of California, Los Angeles to reproduce stick-slip properties of earthquakes. A predictive algorithm is developed using retrospective analysis to identify a precursory chain of events that precede extreme events in the block-and-sandpaper model's synthetic earthquake catalog. This model might help to explore short-term premonitory seismicity patterns.