| 12 Nov 2026
Evaluating the application of clustering analysis on seismic data: an example from Western Quebec Seismic Zone
Abstract. Clustering analysis can identify statistically significant spatial groupings within geoscience data (including earthquakes, landslides, volcanoes and wildfires), providing insight into potential mechanisms influencing the patterns. Previously, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm has been successfully applied in highly active seismic regions to investigate spatial patterns associated with earthquake processes. However, its application is often limited to a small (or a singular) exploration of DBSCANs governing parameters. To demonstrate the capabilities of clustering analysis, here we apply DBSCAN to a large earthquake dataset in a complex intraplate seismic region. For 40 years of earthquakes within Canada's active intraplate Western Quebec Seismic Zone (WQSZ), we explore the full range of input variables to understand the sensitivity of the algorithm to creating cluster scenarios. By evaluating 70+ clustering scenarios generated through different parameter combinations, the results demonstrate that DBSCAN can provide significantly different patterns and therefore a wide range of interpretations. Our finding here is that caution is required when using and interpreting clustering analysis and that a clustering algorithm alone is insufficient for determining the most geologically representative spatial patterns within the region. However, when integrated with temporal analyses and available geological information, DBSCAN-derived clusters identify spatial patterns associated with tectonic features. Specifically, the results show that seismicity within the WQSZ may be influenced by the reactivation of ancient crustal structures inherited from past tectonic events. This study shows the challenges when relying on a small range of clustering analysis input combinations for interpretation, and highlights the importance of evaluating a broad range of parameters when exploring large geoscientific datasets.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Solid Earth.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
