Joint data- and model-driven inversion of elastic and anisotropic parameters in horizontal transversely isotropic media

ABSTRACT Prestack inversion of elastic and anisotropic parameters in horizontally transversely isotropic (HTI) media is a critical technique for characterizing high-angle, directionally aligned fractured reservoirs. Traditional model-driven inversion methods rely on approximate reflection coefficient equations for anisotropic media, but their accuracy and robustness are often limited by model assumptions and data noise. In contrast, data-driven inversion methods based on deep learning suffer from limited generalization and poor physical interpretability, particularly in scenarios with scarce labeled data. We develop a method within a semi-supervised learning framework that integrates data- and model-driven approaches. Under the constraint of the amplitude variation with angle and azimuth reflection coefficient equation, prestack azimuthal seismic data, well-log data, seismic horizon interpretations, and prior models are jointly used to invert elastic and anisotropic parameters in HTI media. Furthermore, the uncertainty of the inversion results is quantified using dropout-based techniques in deep neural networks. Experiments on synthetic and field data show that our joint inversion approach not only improves the accuracy and stability of parameter estimation but also enables effective assessment of inversion uncertainty.