Seismicity analysis using space-time density peak clustering method

This paper presents two-stage clustering approach for accurate analysis of earthquake catalogs where aim is to categorize events in terms of aftershock (AF) clusters or independent backgrounds (BGs). In stage I, the Gaussian kernel-based temporal density estimation is used for grouping of events based on their occurrence time. From the graph, local peak (maxima), local minima and their timing information are utilized to group the events into significant time zones. In stage II, on events of each time zone, coordinate and magnitude information is combined together (weighted mechanism) to determine effective local weighted spatial density (\(\rho ^{\mathrm{w}}\)). Based on \(\rho ^{\mathrm{w}}\) and event distance (\(\delta\)), a decision graph is drawn to find out the spatial cluster centroids for each time zone. Event’s assignment to the centroid is carried out based on its nearest neighbor of higher density. Outliers (non-clustered) are also detected in stage II which is considered as independent backgrounds. The experimental analysis is carried out on historical seismicity of California, Himalaya, Japan and Sumatra–Andaman region. The results indicate that obtained AFs and total number of events follow a similar cumulative and \(\lambda\) rate, whereas BGs have linear cumulative and consistent \(\lambda\) rate. It is also observed that AFs and total events have similar ergodic behavior, quantified from the inverse TM metric plot. The competitive performance of the proposed approach is obtained over state-of-the-art declustering methods.