Data-model jointly driven acoustic impedance inversion

The low-frequency components provided by the well -interpolated prior model for the model -based inversion is usually inaccurate, which usually results in large errors of predicted Acoustic Impedance ( AI) and inferior modeling efficiency via the model-driven method. To alleviate these issues, this paper leverages the preponderance that the data-driven method represented by deep learning inversion can accurately estimate the low-frequency impedance, and investigates the data-model jointly driven AI inversion method. The proposed method combines seismic and well logging data to carry out data-driven and model-driven AI inversion successively. Firstly, the data-driven part utilizes several seismic records at the well locations, well -log derived AI curves, and well -interpolated low-frequency impedance curves to build an intelligent AI prediction network based on Bidirectional Gated Recursive Unit (Bi-GRU). Subsequently, the low-frequency components of estimated AI via the network are used as the data-driven prior model, which replaces the well -interpolated prior model and participates in the model-driven part. Finally, the model -driven part implements the model-based inversion under the joint constraints of seismic data matching and data-driven prior model to obtain the final AI results. Synthetic data and real data tests demonstrate that the proposed method can generate higher accuracy and higher resolution AI results compared with the data-driven or model-driven method. The precise AI results can provide reliable elastic parameter distribution for subsequent reservoir characterization.