Optimizing neural network architectures and using clustering to detect seismic events in noisy ocean bottom seismometer data

Detecting seismic events is essential for monitoring tectonic and volcanic activity, especially in marine environments where noise makes analysis particularly challenging. This study introduces a method that combines Evolutionary Neural Architecture Search (ENAS) with the third generation of the Non-dominated Sorting Genetic Algorithm (NSGA-III) to design and optimize neural networks for seismic event detection using Ocean Bottom Seismometers (OBS) data.

In this work we developed a methodology to analyze heavily noisy data recorded by the TYDE OBS experiment in the southern Tyrrhenian Sea, Italy. In 2000, 14 seismic stations were deployed on the seafloor and around the Aeolian Islands recording data for about 6 months. Stations consisted of wide-band Ocean Bottom Seismometers (OBS) and Hydrophones (OBH).

The preprocessing pipeline includes feature extraction with Discrete Wavelet Transform (DWT) and dimensionality reduction using Principal Component Analysis (PCA), which reduces over 6000 coefficients to just 55 while preserving 95% of the variance. Applied to 90-second overlapping windows, this approach has achieved strong results, with F1 scores exceeding 90% in balanced noisy datasets.

Building on these results, this study explores unsupervised clustering to group similar seismic events and identify possible false positives through anomaly detection. By using adaptive clustering methods that determine the optimal number of clusters based on the data, this approach aims to enhance reliability while providing additional insights into the detected seismic events.

This work highlights the potential of automated tools to complement traditional seismic monitoring methods, balancing accuracy and model complexity while improving efficiency in noise-heavy environments.