Cross-Domain Generalization in Trimodal Self-Supervised Learning for Volcano-Tectonic and Long-Period Seismic Signal Classification

Source abstract

Reliable discrimination of Volcano-Tectonic (VT) and Long-Period (LP) seismic signals is central to volcanic surveillance, yet operational deployment is hindered by pronounced inter-volcano domain shift, scarce annotated catalogues, and heterogeneous acquisition instrumentation. We present a self-supervised trimodal deep learning framework that jointly encodes spectrograms, raw one-dimensional waveforms, and physical tabular features, and we evaluate its capacity for cross-domain generaliza-tion across three leave-one-volcano-out transfer settings among Galeras, Cumbal, and Chiles-Cerro Negro (Colombia). Beyond classification accuracy, we systematically characterise the geometry of the learned latent space using class- and domain-separability landscapes projected into two dimensions, quantifying silhouette coefficients, Davies–Bouldin indices, separation indices, and decision-boundary alignment for every ablation variant. The trimodal framework (SSL Trimodal) attains the highest bootstrap balanced accuracy on the Galeras+Cumbal→Chiles-Cerro Negro transfer (0.9080±0.0072) and the most favourable class-separation index (Sep-Idx2D = 2.06) among all eight evaluated variants in that scenario. It further achieves the best class-separation index (1.72) even on the most divergent transfer setting, Galeras+Chiles-Cerro Negro→Cumbal, despite not attaining the highest accuracy there. This dissociation between point accuracy and embedding quality is, we argue, the more in-formative signal for assessing cross-domain robustness than accuracy alone. We report this result alongside its limitations transparently: the CORAL covariance-alignment baseline outperforms the trimodal framework on two transfer settings with comparable mean accuracies ( 0.81), underscoring that discriminative contrastive pretraining and second-order domain alignment address complemen-tary, rather than redundant, failure modes. To our knowledge, this is the first trimodal self-supervised framework reported for cross-domain generalization in seismo-volcanic signal classification.