Decoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting

Abstract

Abstract This study investigates the collaborative application of fractal clustering patterns and cumulative Coulomb stress (CCS) in the context of earthquake precursory signal identification. We evaluated CCS created by the events based on the period when the Correlation fractal dimension (Dc) commenced falling into relatively lower values. We tested this approach to four strong (M > 7) earthquakes of southern and Baja California, revealing a correlation between these parameters. The crustal readjustment period prior to large earthquakes frequently exhibits a succession of events that result in positive CCS and a higher degree of spatial clustering, indicating low Dc. Preceding strong earthquakes, positive CCS values have been observed concurrently with the onset of low Dc, indicating the potential significance of Dc in seismic hazard assessment studies. We examined these parameters in the Ridgecrest and Baja California regions following the 2010 Mw 7.2 and 2019 Mw 7.1 events. Signs of strain were observed in the northwestern region of the epicenters, indicated by the presence patch of low Dc and positive CCS. We observed that earthquake frequency is typically highest in regions with low to medium Dc values. Multiple sections of the Garlock Fault, manifested by low Dc regions, are loaded, posing a significant seismic risk in Southern California. Similarly, the southern segment of the San Andreas fault displays demonstrate low Dc and high stress, has been inactive for a prolonged period. While these faults may be inactive, we must not underestimate the unpredictability of earthquakes.