RNN‐DAS: A New Deep Learning Approach for Detection and Real‐Time Monitoring of Volcano‐Tectonic Events Using Distributed Acoustic Sensing

Abstract

We present a novel Deep Learning model based on recurrent neural networks (RNNs) with long short-term memory (LSTM) cells, designed as a real-time volcano-seismic signal recognition system for distributed acoustic sensing (DAS) measurements. The model was trained on an extensive database of volcano-tectonic events derived from the co-eruptive seismicity of the 2021 La Palma eruption, recorded by a High-fidelity submarine distributed acoustic sensing array near the eruption site. The features used for supervised model training, based on the average signal energy in frequency bands, enable the model to effectively leverage the spatio-temporal contextual information of seismo-volcanic signals provided by the technique. The proposed model not only detects the presence of volcano-tectonic events but also analyzes their temporal evolution, selecting and classifying their complete waveforms with an accuracy of approximately 97%. Furthermore, the model has demonstrated robust performance in generalizing to other time intervals and volcanoes. Such results highlight the potential of using RNN-based approaches with LSTM cells for DAS systems located in volcanic regions, enabling fast, automatic analysis with low computational requirements and minimal retraining. This allows continuous real-time monitoring of seismicity while facilitating the creation of labeled seismic catalogs directly from DAS measurements, representing a significant advancement in using DAS technology as a viable tool to study active volcanoes and their seismic activity.