Continuous Active Learning for Seismo-Volcanic Monitoring

Deep learning has advanced seismo-volcanic monitoring to unprecedented performance levels. Nevertheless, seismic data labeling still requires substantial annotation efforts, often delayed in time if the eruptive state alters the data conditions. The selective segmentation of which earthquake transients have to be reviewed by an expert can significantly reduce annotation time, speed up algorithmic training, and boost monitoring adaptability to unforeseen situations. In this work, we propose a Bayesian temporal convolutional neural network (B-TCN) to perform continuous detection and classification while extracting the most uncertain events from the continuous data stream. Formulated as an active learning (AL) procedure, our B-TCN outputs an uncertainty map over time, highlighting the class memberships that are needed to be reviewed. We attain a significant improvement in monitoring metrics, with only a fraction of the initial dataset to achieve a recognition performance of 83% for four seismo-volcanic events.