Clustering of Earthquake Sequence and Its Effect on b Value in North China

Abstract

Spatio-temporal clustering is a significant characteristic of earthquake sequence activity. The reasonable identification of background and cluster events is an important foundation for earthquake forecasting and seismic hazard assessment. The two data-driven declustering algorithms based on stochastic point processes and nearest-neighbor distances were used to decluster the North China. The impact of declustering on the Gutenberg-Richter b value is analyzed. The results indicate that the two algorithms have similar characteristics in terms of spatio-temporal declustering characteristics, sequence identification, and evolution of b value. Both algorithms remove the influence of significant earthquakes. The seismic activity exhibits complex spatio-temporal characteristics in North China, with certain foreshocks occurring before moderate and strong earthquakes. The trend of the declustering and non-declustering b values is largely consistent, which indicates that the declustering does not alter the essential change characteristics of seismic activity. The nearest neighbor algorithm tends to remove a higher proportion of small earthquakes, resulting in a decrease in the b value. The background b value of the stochastic declustering method is comparable to the entire catalog, and both are higher than the nearest neighbor method, indicating that the first event in an earthquake sequence tends to be smaller, traditionally considered a foreshock. The stochastic declustering method did not significantly modify the statistical properties of the earthquake catalog, while the declustered catalog using the nearest neighbor method showed a more pronounced Poisson characteristic. The research results can provide basic data support for sequence tracking, moderate-to-strong earthquake risk assessment, and earthquake forecasting model construction in North China.