Physically driven feature engineering for deep learning applications in seismo-volcanic signal analysis

The progressive growth of seismological databases has motivated the exploration of novel methodologies for common tasks such as detection and phase-picking, with a focus on maintaining reliability comparable to human performance. This goal consistently involves leveraging deep learning techniques, which emulate sensory processing in the human brain through numerical simulations. This study introduces a physically driven feature engineering approach that capitalizes on the inherent information within seismic data. While many contemporary studies train their models via robust raw datasets, practical alternatives tailored for smaller databases are often overlooked. Feature engineering in seismological contexts aims to develop deep learning models with tangible physical significance, specifically those that target event detection and phase-picking tasks across both local and regional seismic environments. Our approach involves physically driven transformations that incorporate amplitude spectra from signals filtered at predefined frequency bands, as well as spatial features such as angles computed from signal components like wave incidence and azimuth. This methodology is particularly pertinent in seismo-volcanic contexts, where accurate discrimination and characterization of seismic signals are pivotal for monitoring and risk assessment purposes. The incorporation of significant physical information from seismic signals into pattern recognition is crucial, as many feature engineering applications lack a contextual understanding of the data, which can lead to distortions, particularly within geophysical domains. Our results demonstrate human-level performance in these common tasks, harnessing the capabilities of statistical learning algorithms as a practical, resource-efficient solution for addressing these challenges on a large scale.