Multi-station automatic classiﬁcation of seismic signatures from the Lascar volcano database

. This study was aimed to build a multi-station automatic classiﬁcation system for volcanic seismic signatures such as hybrid, long period, tremor, tectonic and volcano-tectonic events. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed ﬁve experiments using different datasets with data that was real, synthetic, and two different combinations of these (combined 1 and combined 2), and balanced subset without synthetic data. The experiment presented the highest scores when a process of data 5 augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was over-ﬁtted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results reached high predictive performance, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The results obtained showed the performance of the probabilistic model, reaching high scores over different 10 test datasets. The most valuable beneﬁt of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalisable model which, in near future, can be in reach a probabilistic model muti-volcano. The portability generated to apply this methodology will permit to work to different scales of operation of a network (from small temporal networks to world-wide volcano obser-vatories). The acquisition of more generalisable models creates a good opportunity to develop a multi-volcano probabilistic model for volcanoes worldwide. This will improve the understanding and evaluation of the hazards and risks associated with the activity of volcanoes.