Landslide-Induced Seismic Signal Clustering With Outlier Removal

Seismic clustering is a vital technique in seismology for identifying patterns in seismic events and providing insights into geological processes. However, its application to ongoing landslide-induced signals and impact of outliers have not received much research attention. This letter presents a novel consensus clustering strategy with outlier removal for landslide-induced seismic signals. The proposed approach incorporates a parameter setting method to improve clustering accuracy and robustness. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art clustering methods.