Feature-data Collaborative Inversion: A Siamese Convolutional Neural Network Method for Shallow-Subsurface EFWI

Elastic full-waveform inversion (EFWI) constitutes a vital tool for high-resolution subsurface imaging. However, its application in shallow-subsurface exploration is hindered by strong nonlinearity, extreme sensitivity to the initial model, and the low sensitivity of specific parameters, particularly density. To address these challenges, we introduce a Siamese CNN-based EFWI framework (SCFWI) for multi-parameter inversion. This framework embeds a weight-sharing Siamese network into the physical inversion loop, adaptively extracting multi-scale common features from both observed and synthetic seismic data. We construct a novel feature-data collaborative objective function that imposes dual constraints: minimizing feature discrepancies to recover global structures and constrain low-sensitivity parameters, while simultaneously reducing waveform residuals to preserve local high-resolution details. Numerical experiments demonstrate that SCFWI significantly mitigates reliance on accurate initial models and enhances noise robustness. Notably, the method achieves a 56.6% reduction in the density root-mean-square error (RMSE) for the SEAM model compared to conventional EFWI, highlighting its capability to mitigate parameter crosstalk. This study presents a promising physics-guided deep learning framework for multi-parameter EFWI, offering valuable insights for broader intelligent geophysical inversion applications.