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In several application areas, such as medical diagnosis, spam filtering,fraud detection, and seismic data analysis, it is very usual to find relevantclassification tasks where some class occurrences are rare. This is the socalled class imbalance problem, which is a challenge in machine learning. Inthis work, we propose the SeismoFlow a flow-based generative model to createsynthetic samples, aiming to address the class imbalance. Inspired by the Glowmodel, it uses interpolation on the learned latent space to produce syntheticsamples for one rare class. We apply our approach to the development of aseismogram signal quality classifier. We introduce a dataset composedof5.223seismograms that are distributed between the good, medium, and badclasses and with their respective frequencies of 66.68%,31.54%, and 1.76%. Ourmethodology is evaluated on a stratified 10-fold cross-validation setting,using the Miniceptionmodel as a baseline, and assessing the effects of addingthe generated samples on the training set of each iteration. In ourexperiments, we achieve an improvement of 13.9% on the rare class F1-score,while not hurting the metric value for the other classes and thus observing theoverall accuracy improvement. Our empirical findings indicate that our methodcan generate high-quality synthetic seismograms with realistic looking andsufficient plurality to help the Miniception model to overcome the classimbalance problem. We believe that our results are a step forward in solvingboth the task of seismogram signal quality classification and class imbalance.

SeismoFlow -- Data augmentation for the class imbalance problem