Cubism: Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer

Volcanic eruptions are severe global threats. Forecasting these unrests via monitoring precursory earthquakes is vital for managing the consequent economic and social risks. Due to various contextual factors, volcano-seismic patterns are not spatiotemporal invariant. Training a robust model for any novel volcano-seismic situation relies on a costly, time-consuming and subjective process of manually labeling data; using a model trained on data from another volcano-seismic setting is typically not a viable option. Unsupervised domain adaptation (UDA) techniques address this issue by transferring knowledge extracted from a labeled domain to an unlabeled one. A challenging problem is the inherent imbalance in volcano-seismic data that degrades the efficiency of an adopted UDA technique. Here, we propose a co-balanced UDA approach, called Cubism, to bypass the manual annotation process for any newly monitored volcano by utilizing the patterns recognized in a different volcano-seismic dataset with labels. Employing an invertible latent space, Cubism alternates between a co-balanced generation of semantically meaningful inter-volcano samples and UDA. Inter-volcano samples are generated via the mixup data augmentation technique. Due to the sensitivity of mixup to data imbalance, Cubism introduces a novel co-balanced ratio that regulates the generation of inter-volcano samples considering the conditional distributions of both volcanoes. To the best of our knowledge, Cubism is the first UDA-based approach that transfers volcano-seismic knowledge without any supervision of an unseen volcanoseismic situation. Our extensive experiments show that Cubism significantly outperforms baseline methods and effectively provides a robust cross-volcano classifier.