Explaining Deep Learning Models in Full Waveform Inversion: Enhancing Transparency in Seismic Data Interpretation

We present a physics‐informed neural network approach for full‐waveform inversion (FWI) that embeds a Deep Image Prior (DIP) U-Net within the inversion loop and applies explainable AI (XAI) methods to interpret the learned model. Starting from a decimated Marmousi velocity model, we generate synthetic seismic data via 2D acoustic wave propagation. A smoothed version of the true model initializes a randomly initialized U-Net (the DIP), which is pretrained to replicate the smoothed model. Thereafter, the DIP's weights replace the cell‐wise velocity parameters in FWI, and an iterative optimizer adjusts these weights to minimize discrepancies between observed and simulated waveforms. The resulting high‐resolution velocity model arises from physics‐constrained learning. To reveal the network's decision process, we compute attribution maps using SHapley Additive exPlanations (SHAP), Integrated Gradients, and occlusion sensitivity. These maps highlight the spatial features; primarily high‐contrast layers and anomalies that drive the DIP's output. Comparative analyses of true vs. predicted models, residuals, and combined depth/lateral sensitivity profiles confirm that the deep model captures essential subsurface structures. This framework enhances transparency in seismic inversion by linking data‐driven models to geophysical knowledge, thus bridging the gap between "black‐box" deep learning and interpretable imaging.