Prediction Of Large Earthquakes Using Pattern Recognition Method And Chaos Theory

ABSTRACT

The prediction of Large Earthquakes (LE) constitutes a global challenge. Several methods of earthquake prediction including Pattern Recognition (PR) have been proposed but sometimes produce patterns that are not suitable. Occurrence of earthquakes is always assumed to be random when these methods produce unreliable pattern. The fact that these patterns considered to be random could be chaotic (predictable but difficult) has not been investigated. This study was designed to use chaos theory to investigate patterns of occurrence of earthquakes where PR method gives unreliable pattern. Earthquake data (1899-2009) of the Circum-Pacific seismic zone were extracted from the catalogue of Advanced National Seismic System (USA). The zone is the source of 90% of the world's earthquakes and the one with most recorded data. The zone was divided into five regions [Rl(Lat 55° to 67° ; Long - 170° to -145°), R2(Lat 32° to 44° ; Long 134° to 148°), R3(Lat 39° to 50° ; Long 140° to 157°), R4(Lat -42° to -29° ; Long -80° to -66°), R5(Lat 48° to 54° ; Long - 179° to -160°)] based on the pattern of occurrence of small earthquakes. Events in each region were divided into constant time intervals and annular width of 100 km for the investigation of temporal and spatial distribution of the earthquakes respectively. The PR method was applied to the data in each time interval and annular width, and the pattern monitored using seismic b-values and the locations of the maximum seismic energy. The b-values were determined from GutenbergRichter law using the linear curve fitting method, while the locations of the maximum seismic energy were determined using Compicat program. Using chaos theory, the phase space plots of the seismic activities were constructed to determine the space clustering of the seismic events associated with LE. The Lyapunov Exponent (LEX) and its spectrum were obtained using Wolf and Sprott procedures to provide a picture of the system's dynamics and determine whether it is random or chaotic.