Pattern Informatics Approach to Earthquake Forecasting in 3D

Yuzo Toya, Kristy F. Tiampo, John B. Rundle, Chien-chih Chen, Hsien-Chi Li, & William Klein

Published November 17, 2009, SCEC Contribution #1243

Natural seismicity is correlated across multiple spatial and temporal scales, but correlations in seismicity prior to a large earthquake are locally subtle (e.g. seismic quiescence) and often prominent in broad scale (e.g., seismic activation), resulting in local and regional seismicity patterns, e.g. a Mogi’s donut. Recognizing that patterns in seismicity rate are reflecting the regional dynamics of the directly unobservable crustal stresses, the Pattern Informatics (PI) approach was introduced by Tiampo et al. in 2002, Rundle et al., 2002. In this study, we expand the PI approach to forecasting earthquakes into the third, or vertical dimension, and illustrate its further improvement in the forecasting performance through case studies of both natural and synthetic data. The PI characterizes rapidly evolving spatio-temporal seismicity patterns as angular drifts of a unit state vector in a high dimensional correlation space, and systematically identifies anomalous shifts in seismic activity with respect to the regional background. 3D PI analysis is particularly advantageous over 2D analysis in resolving vertically overlapped seismicity anomalies in a highly complex tectonic environment. Case studies will help to illustrate some important properties of the PI forecasting tool.

Key Words
pattern informatics, effective ergodicity, earthquake forecasting, seismicity rate

Toya, Y., Tiampo, K. F., Rundle, J. B., Chen, C., Li, H., & Klein, W. (2009). Pattern Informatics Approach to Earthquake Forecasting in 3D. Concurrency and Computation, 22(12), 1569-1592. doi: 10.1002/cpe.1531.