Hybrid Physics-Data-Driven Framework for Blind Acoustic Impedance Inversion

Abstract

Conventional acoustic impedance inversion methods have long faced technical bottlenecks such as inaccurate wavelet estimation and strong dependence on initial models. Although existing deep learning approaches can partially alleviate these problems, they often compromise model simplicity and training efficiency, while introducing new challenges such as limited generalizability and heavy reliance on labeled data. To overcome these limitations, this study proposes a lightweight inversion framework that tightly integrates physics-driven and data-driven paradigms. The physics-driven component adopts a neural network architecture largely consistent with traditional modeling processes, enabling direct optimization of physically meaningful parameters through backpropagation, thereby avoiding the construction of excessively complex inverse operators. Meanwhile, regularization methods are introduced to enforce geological prior knowledge (that is, the “layered geological model” assumption) on the network parameters, improving the spatial continuity of the reconstructed impedance models. The data-driven component employs an enhanced 2D U-Net integrated with Class Activation Mapping (U-Net-CAM) to generate accurate reference models from sparse well-log data. Tests on both synthetic and field datasets demonstrate the advantages of the proposed method: (1) physically interpretable network design; (2) strong robustness to noise and reduced dependence on training data; (3) higher accuracy and better spatial continuity compared to conventional and purely data-driven methods. This work provides a new perspective for addressing long-standing challenges in seismic impedance inversion.