Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion

TL;DR

Neural network surrogate models in geophysics can learn physically meaningful sensitivity kernels from surface-wave dispersion data, aiding inversion and uncertainty analysis.

Contribution

This work demonstrates that data-driven neural networks can recover physical sensitivity structures, not just data mappings, in surface-wave dispersion modeling.

Findings

Neural gradients match main depth-dependent kernel structures across periods.

Strong priors can introduce artifacts into inferred sensitivities.

Neural surrogates can be useful for physical interpretation and inversion.

Abstract

Data-driven neural networks are increasingly used as surrogate forward models in geophysics, but it remains unclear whether they recover only the data mapping or also the underlying physical sensitivity structure. Here we test this question using surface-wave dispersion. By comparing automatically differentiated gradients from a neural-network surrogate with theoretical sensitivity kernels, we show that the learned gradients can recover the main depth-dependent structure of physical kernels across a broad range of periods. This indicates that neural surrogate models can learn physically meaningful differential information, rather than acting as purely black-box predictors. At the same time, strong structural priors in the training distribution can introduce systematic artifacts into the inferred sensitivities. Our results show that neural forward surrogates can recover useful physical information for inversion and uncertainty analysis, while clarifying the conditions under which this differential structure remains physically consistent.