Unsupervised deep learning on seismic data to detect volcanic unrest
- 1Istituto Nazionale di Geofisica e Vulcanologia – Osservatorio Etneo, Piazza Roma 2, 95123 Catania, Italy
- 2Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Sezione di Scienze della Terra, Università degli Studi di Catania, Corso Italia 57, I-95129, Catania, Italy
- 3PeRCeiVe Lab – Department of Electrical, Electronics and Computer Engineering – University of Catania, viale Andrea Doria, 6, I-95125 Catania, Italy
- 4Istituto Nazionale di Geofisica e Vulcanologia – sezione di Pisa, via della Faggiola, 32, 56126 Pisa, Italy
The significant efforts of the last years in new monitoring techniques and networks have led to large datasets and improved our capabilities to measure volcano conditions. Thus nowadays the challenge is to retrieve information from this huge amount of data to significantly improve our capability to automatically recognize signs of potentially hazardous unrest.
Unrest detection from unlabeled data is a particularly challenging task, since the lack of annotations on the temporal localization of these phenomena makes it impossible to train a machine learning model in a supervised way. The proposed approach, therefore, aims at learning unsupervised low-dimensional representations of the input signal during normal volcanic activity by training a variational autoencoder (VAE) to compress, reconstruct and synthesize input signals. Thanks to the internal structure of the proposed VAE architecture, with 1-dimensional convolutional layers with residual blocks and attention mechanism, the representation learned by the model can be employed to detect deviations from normal volcanic activity. In our experiments, we test and evaluate two techniques for unrest detection: a generative approach, with a bank of synthetic signals used to assess the degree of correspondence between normal activity and an input signal; and a discriminative approach, employing unsupervised clustering in the VAE representation space to identify prototypes of normal activity for comparison with an input signal.
How to cite: Cannavo', F., Cannata, A., Palazzo, S., Spampinato, C., Faraci, D., Castagnolo, G., Kavasidis, I., Montagna, C., and Colucci, S.: Unsupervised deep learning on seismic data to detect volcanic unrest, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18631, https://doi.org/10.5194/egusphere-egu2020-18631, 2020