SOM-based approach for seismic data analysis in the Campi Flegrei Caldera using Multiscale Entropy (MSE)

The Campi Flegrei caldera, a high-risk volcanic region in southern Italy, is currently experiencing an unrest phase characterized by significant ground deformation and increasing seismic activity, including events with magnitudes up to Md 4.2 recorded in September 2023. The continuous availability of seismic data provides a valuable framework for evaluating and developing novel event detection methods. However, in regions characterized by extensive natural and anthropogenic noise, the resulting low signal-to-noise ratio poses a significant challenge to seismic event detection. This limitation is sharpened when analyses are based on data from a single seismic station.

To address this challenge, the present study introduces an innovative methodology designed for single-station analysis that combines the Multiscale Entropy (MSE) algorithm with Self-Organizing Maps (SOM) and the Short-Term Average/Long-Term Average (STA/LTA) technique for seismic signal detection and clustering. Linear Predictive Coding (LPC) algorithm is also employed in conjunction with the SOM map for a preliminary stage to certify the quality of the data and check for anomalies.

The analysis uses continuous seismic data recorded over six months in the Pisciarelli area of the Campi Flegrei caldera, segmented into one-minute windows. Key features, including STA/LTA ratios (computed with 1s and 30s windows) and MSE values (computed over 20 time scales using a coarse-graining operation), are extracted to encode the input vectors for SOM training. The resulting 6x6 SOM map effectively clusters the seismic traces, revealing hidden patterns and distinguishing seismic events from background seismic noise. Notably, approximately 20% of the transient signals within the nodes of the seismic event cluster were identified as uncatalogued events, demonstrating the ability of the method to detect previously unrecorded activity. In addition, the map includes different clusters that highlight the influence of environmental factors, such as precipitation occurrences or volcanic fluid emissions, on the seismic waveforms.

The integration of complexity-based analysis of the MSE alongside conventional STA/LTA techniques enables improved single-station event detection, even in a noisy environment, and hints at the correlation between seismic signal complexity and volcano dynamics. These results highlight the potential of advanced clustering and feature extraction techniques to refine seismic monitoring in active volcanic environments.