Multi-parameter Pre-Stack Seismic Inversion Based on Deep Learning with Sparse Reflection Coefficient Constraints

In the field of seismic inversion, Convolutional Neural Network (CNN) has been extensively applied for their powerful capability of feature extraction and nonlinear fitting. However, the insufficient amount of labeled seismic inversion dataset impedes the application of CNN in seismic elastic inversion. Besides, the lack of effective geophysical constraints in conventional CNN will make the network prone to over-fitting, leading to unstable inversion results. In this paper, a workflow is developed for generating sufficient and diverse datasets for pre-stack seismic inversion with limited log and seismic data. The Sequential Gaussian Co-Simulation algorithm is used to simulate the changes in the reservoir space under the constraints of the low-frequency model. At the same time, the Elastic Distortion algorithm is used to simulate the complex geological structures. This can increase the diversity of the strata longitudinal combination by enriching the combination mode of stratigraphic parameters. Besides, the combination of a U-net and three fully connected networks (UCNN) is proposed to predict the elastic parameters from seismic data. In UCNN, the sparse reflection coefficient is used as a constraint to improve the accuracy of the network. The performance of this method was evaluated by synthetic and field data examples. The results show not only the effectiveness of the proposed method but also demonstrate its outperformance over the conventional deep learning method. The R2 scores of density, Vp and Vs are 0.94, 0.98, 0.98.