Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function

Abstract

Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance, which is significant in reservoir description and identification. Based on proper initial models, most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy, but not high resolution. Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and high-resolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks. However, these methods require a significant number of training samples, which are frequently insufficient. To obtain P-wave impedance with both high accuracy and high resolution, we propose a model-data-driven inversion method using ResNets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise. By using initial models and training samples, the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution. Tests on synthetic and field data demonstrate the proposed method's efficacy and practicability.