Decoding the Dynamics of the 2021 Fukutoku-Oka-no-Ba Submarine Eruption via Interpretable Spectral-Hilbert Representations (Spec2Vec)

Submarine volcanism represents one of the dominant forms of magmatic output on Earth, yet our understanding of the underlying eruptive processes remains limited compared to subaerial systems. The remote locations and lack of direct visual observations often obscure the dynamics of these eruptions, where the interaction with the hydrostatic load and phase changes (steam/water) creates distinct physical regimes. In the absence of near-field observations, far-field hydroacoustic records—propagated over thousands of kilometers in the SOFAR channel—provide a critical, high-temporal-resolution window into these deep-sea events. However, a significant challenge lies in processing the high volume of continuous data to categorize signals into physically meaningful regimes without relying on manual classification or subjective thresholds.

 

In this study, we present Spec2Vec, a novel framework for the unsupervised classification of hydroacoustic time series, applied to the major 2021 Fukutoku-Oka-no-Ba shallow submarine eruption. Current unsupervised approaches in geo-acoustics often rely on decomposition methods like Non-Negative Matrix Factorization (e.g., SPECUFEX), Independent Component Analysis (ICA), the scattering transform, or latent representations from neural network auto-encoders. While effective, these methods can be computationally intensive or result in "black box" features lacking direct physical intuition. In contrast, Spec2Vec utilizes Hilbert space-filling curves to topologically map 2D time-frequency representations into a 1D sequence, strictly preserving multi-scale locality. From this linearized stream, we extract a compact set of entropy and scaling features. This approach captures the "texture" of the spectrogram—the specific arrangement of energy in time and frequency—more uniquely and efficiently than standard modal image features. The resulting feature space is fast to compute and highly interpretable, bridging the gap between raw acoustic data and physical source mechanics.

 

We evaluate this feature set by applying it to ten days of continuous hydroacoustic data from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) International Monitoring System (IMS), capturing the main eruption sequence as well as pre- and post-eruptive phases. Through unsupervised learning, Spec2Vec automatically organizes the complex acoustic stream into coherent clusters. To validate the physical interpretability of these clusters, we correlate the unsupervised classes with independent eruptive proxies, including satellite-derived lightning data, plume height, and mass eruption rates. Furthermore, we inject synthetic acoustic source models—simulating single bubble oscillations, turbulent jets, bubble plumes, hydroacoustic earthquakes, explosions, and volcanic tremor—into the dataset to map clusters to specific source mechanisms.

 

Our results offer a rare, data-driven characterization of the Fukutoku-Oka-no-Ba eruption, identifying distinct phases of jetting and tremor that align with atmospheric observations. This demonstrates that Spec2Vec serves not merely as a feature generation tool, but as a generalizable engine for the automated discovery of physical processes in complex geophysical time series. This approach holds significant potential for scaling the analysis of global hydrophone datasets, enabling the systematic distinction and quantification of eruption rates and processes across the global submarine volcanic inventory.