📝 SummarXiv

Abstract

Deep learning is an increasingly popular approach for inverting surface wave dispersion curves to obtain Vs profiles. However, its generalizability is constrained by the depth and velocity scales of training data. We propose a unified deep learning framework that overcomes this limitation via normalization of dispersion curves. By leveraging the scaling properties of dispersion curves, our approach enables a single, pre-trained model to predict Vs profiles across diverse scales, from shallow subsurface (e.g., < 10 m depth) to crustal levels. The framework incorporates a novel transformer-based model to handle variable-length dispersion curves and removes tedious manual parameterization. Results from synthetic and field data demonstrate that it delivers rapid and robust inversions with uncertainty estimates. This work provides an efficient inversion approach applicable to a wide spectrum of applications, from near-surface engineering to crustal imaging. The framework establishes a paradigm for developing scale-invariant deep learning models in geophysical inversion.