Influence of Seismic Monitoring Capability on Foreshock Identification in California: A Comparative Analysis of Methods

Foreshocks are among the few observable phenomena preceding many large earthquakes and hold potential for short-term earthquake forecasting. However, their identification and interpretation remain challenging, particularly due to variations in seismic monitoring capabilities, such as the magnitude of completeness (M_c) in earthquake catalogs. This study investigates the impact of M_c on the proportion of mainshocks identified with foreshocks (P_f) in California, using four popular methods: the Space-Time Window (STW) method, Nearest-Neighbor Clustering (NNC) method, Empirical Statistical (ES) method, and the Epidemic-Type Aftershock Sequence (ETAS) model.

Results show that P_f estimated by the STW method strongly depends on M_c, with higher M_c values leading to lower P_f due to the misclassification of background events as foreshocks. In contrast, the NNC and ES methods yield more consistent P_f values across different M_c thresholds, though the ES method reports slightly lower P_f due to its sensitivity to background seismicity rates. The ETAS model reveals that at low M_c, a greater proportion of foreshocks are associated with aseismic processes, whereas at high M_c, distinguishing between aseismic and cascade-driven mechanisms becomes increasingly challenging. These findings suggest that enhanced seismic monitoring has limited effects on P_f, but is essential for identifying the underlying processes driving foreshocks.