Detection of Seismic Events Using Semi-Supervised Learning

The continuous seismic monitoring provides numerous waveform signals that provide useful information about geological processes such as landslides and geohazard activities. In tectonically active zones like the Himalayan arc, mass-movement events often overlap with natural earthquakes, creating challenges for reliable event discrimination. This study presents a semi-supervised learning framework that detects landslides like debris flow, rockfall, as well as earthquakes, from continuous seismological data with the help of a small amount of labelled dataset, calculating physically interpretable attributes from waveforms. The waveform and spectrum-based 157 features were extracted from segmented seismic windows, representing temporal, spectral, energy, and morphological attributes. The workflow combines dimensionality reduction, density-based clustering, and graph-based label propagation to identify and classify seismic events. To evaluate methodological choices, two complementary studies were conducted. The model benchmarking study compared four combinations of embedding and clustering algorithms, and a parametric sensitivity analysis that investigated the influence of key hyperparameters of the embedding and clustering algorithms. Feature importance analysis using statistical and machine-learning-based techniques was integrated throughout the study to ensure physical interpretability and to identify attributes most relevant for source discrimination. In this study, three months of continuous seismological data of the Tehri region, Uttarakhand, were analysed and revealed many previously undetected events. Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, followed by Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised event grouping, provided the best performance for seismic event detection. This approach effectively identified seismic events that would be difficult to observe using conventional methods. The proposed approach is well-suited for large-scale seismic monitoring applications where labelled data are limited and provides a broad application for geohazard detection and operational seismic analysis.