Detection of seismic events via cluster analysis of trimmed spectrograms

Due to the growth of data volumes in monitoring seismicity, automation is crucial to fully use the information content. Seismic event detection is the first step in processing, aimed at reducing the amount of data by extracting intervals with seismic activity. At the same time, interpretability remains important for justification and quality control of the detected events, especially when multiple candidate events are to be triaged. A novel seismic event detection technique based on cluster analysis of trimmed spectrograms (CATS) is presented. It uses an automatic noise estimator, B-E-DATE, and cluster filtering in the time-frequency domain. CATS workflow is fully transparent, offering a framework for comprehensive quality control. Evaluated on the benchmark dataset TXED, CATS showed competitive performance compared to traditional and deep learning methods by showing high recall, precision, and F-scores. Furthermore, CATS can be extended to multi-station processing and employed directly for denoising without requiring any extra parameters, and its outputs can be utilized for phase-picking and event classification.