Spec2VEC : Interpretable Spectral Representations of Planetary Signals in Vector Spaces

Abstract

Summary The application of machine learning to planetary geophysics is frequently bottlenecked by a scarcity of labeled data and the complexity of non-stationary seismo-acoustic signals. While deep learning offers powerful feature extraction, it requires massive datasets that are rarely available for phenomena like Martian quakes or deep-ocean volcanism. Consequently, researchers often rely on unsupervised machine learning or hand-engineered features that are typically impoverished or hard to interpret. To resolve this, we introduce Spec2VEC, a framework that transforms parameter-dependent TFRs into a robust, interpretable, and high-dimensional vector space. Spec2VEC treats spectrograms not merely as energy distributions but as structured, textured images, and extracts physics-informed, dataset-agnostic features without requiring massive training loops. The framework systematically extracts a feature vector using two novel pathways: (1) a Textural path that utilizes Hilbert space-filling curves to map 2D spectral patterns into 1D sequences for advanced information-theoretic analysis (e.g., permutation entropy, fractal dimension); and (2) a Spatial path driven by PDFSI (Probabilistic Distribution Fitting for Signal Identification), a novel probabilistic pre-conditioning engine that quantifies spatial connectivity by dynamically separating coherent signal modes from stochastic noise. We validate this feature space on a comprehensive benchmark of complex synthetic wavefields and two real-world examples: an Exotic Seismic Events Catalog (ESEC) and a Distributed Acoustic Sensing (DAS) dataset. Spec2VEC provides objective features for information discrimination and enables highly accurate classification of the diverse geophysical signal classes present in the ESEC catalog. Furthermore, application on the high data volume DAS dataset confirms its computational efficiency and domain-agnostic capability to isolate anomalous seismic arrivals from background noise. Ultimately, Spec2VEC bridges the interpretability of classical signal processing with the discriminative power of modern representation learning, providing a reliable, interpretable foundation for automated discovery across the geosciences.