Three physical guidance strategies for acoustic impedance estimation

In deep learning-based inversion, physics-guided strategies have been widely applied to improve the accuracy and robustness of acoustic impedance (AI) estimation. This study investigates the impact of three physics-guided strategies on acoustic impedance estimation: 1) Constructing physics-guided pseudo-labels, which enhances the model’s understanding of real physical processes by embedding physical knowledge into the generated pseudo-labels; 2) Physics-guided loss function, which integrates physical constraints into the loss function, encouraging the network to learn features that are more consistent with actual physical laws; 3) Transfer learning, which achieves better estimation performance on limited datasets through pre-training and fine-tuning strategies. Experimental results demonstrate that the use of physics-guided strategies can effectively improve the performance of deep learning models in complex environments, particularly in cases of data scarcity or high noise, significantly enhancing the accuracy and stability of acoustic impedance inversion.