2012/08/07 by John B. Rundle, J. R. Holliday, William R. Graves +3 · 6 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #Computer science #Distributed Sensor Networks and Detection Algorithms #Mathematics #Neural Networks and Applications #Physics #Statistical physics
paper · doi:10.1103/physreve.86.021106
openalex publication_date 2012/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many driven threshold systems display a spectrum of avalanche event sizes, often characterized by power-law scaling. An important problem is to compute probabilities of the largest events ("Black Swans"). We develop a data-driven approach to the problem by transforming to the event index frame, and relating this to Shannon information. For earthquakes, we find the 12-month probability for magnitude m>6 earthquakes in California increases from about 30% after the last event, to 40%-50% prior to the next one.