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

Research demonstrates that neural networks trained as data-driven forward models can autonomously learn physical sensitivity kernels—mathematical representations of how system outputs respond to parameter changes. The finding is evidenced through surface-wave dispersion analysis in seismic modeling, suggesting deep learning models develop interpretable physics-aligned representations without explicit instruction.