An Intelligent Prestack Seismic Inversion Framework Using the Propagator Matrix Method

Abstract

Simultaneous inversion of P-wave velocity, S-wave velocity, and density remains a major challenge in quantitative seismic interpretation. Conventional inversion techniques based on the Zoeppritz equations and their approximations often fail to adequately capture the complexity of wave propagation in layered media, especially in the presence of interbed multiples and mode conversions. These limitations become particularly evident in waveform-matching-based inversion methods. To address this issue, we propose an intelligent inversion framework constrained by the propagator matrix (PM) method, in which a differentiable wave-propagation operator is embedded into a data-driven bidirectional gated recurrent unit (Bi-GRU) network. Compared with approaches that characterize only single-interface reflection/transmission behavior, the introduced PM module provides a more complete 1-D wave-propagation description, thereby enhancing well-to-seismic tie quality. The PM module is then used to constrain both the network’s learning over the entire reservoir interval and the prediction process. Both synthetic and field experiments demonstrate that, compared with a purely data-driven inversion and a Zoeppritz-constrained model-driven approach, the proposed method achieves improved accuracy and stability. Integrating the PM method with deep learning (DL) provides an effective strategy for physics-constrained, high-fidelity elastic-parameter inversion.