High-resolution pre-stack seismic data are essential for exploring complex and thin-layered reservoirs, and can enhance the reliability of resource evaluation. However, traditional pre-stack seismic data resolution enhancement methods are single-trace processing unable to preserve the amplitude-versus-offset (AVO) relationships, thus compromising the accuracy of subsequent inversion and interpretation. To address the limitation, a novel semi-supervised learning framework is proposed, enhancing the resolution of pre-stack seismic data and preserving AVO relationships of resolution-enhanced seismic data. The proposed approach integrates physical models with well-log data to train neural networks within a hybrid data- and model-driven framework. The labelled dataset, constructed from well-log data and corresponding amplitude-versus-angle (AVA) gathers, eliminates the assumptions inherent in traditional single-trace reflection coefficient inversion methods. Meanwhile, the physics-guided component utilizes unlabeled seismic data to train neural networks, improving the generalizability of the method. Importantly, this approach preserves AVO relationships during resolution enhancement by leveraging well-log data and the Zoeppritz equation that are essential for elastic parameters inversion. Experiments on both two-dimensional synthetic data and a three-dimensional field data demonstrate that the proposed method can stably extend the frequency bandwidth by 40%, proving its feasibility and effectiveness of the proposed approach.

Gas-hydrate saturation and porosity are the most crucial reservoir parameters for gas-hydrate resource assessment. Numerous academics have put forward elastic and electrical petrophysical models for calculating the saturation and porosity of gas-hydrate. However, owing to the limitations of a single petrophysical model, the estimation of gas-hydrate saturation and porosity using single elastic or electrical measurement data appears to be inconsistent and uncertain. In this study, the sonic wave velocity, density and resistivity well log data are combined with a Bayesian linear inversion method for the simultaneous estimation of gas-hydrate saturation and porosity. The sonic wave velocity, density and resistivity data of the Shenhu area in the South China Sea are used to estimate the gas-hydrate saturation and porosity. To validate the accuracy of this method, the estimation results are compared with the saturation obtained from pore water chemistry and porosity obtained from density logs. The well log data examples show that the joint estimation method not only provides a rapid estimation of the gas-hydrate reservoir parameters but also improves the accuracy of results and determines their uncertainty.