Multi-Task Seismic Deep Learning Inversion Based on FCRN and GRU Hybrid Network

Seismic elastic parameter inversion enables the transformation of seismic data into subsurface structures and physical parameters of formations. However, due to the intricate geological structure, deep learning methods have been discovered to produce more precise inversion results than traditional methods. Nevertheless, inverting multiple elastic parameters individually is both time-consuming and prone to causing significant errors for the ignorance of interconnections among the parameters. Therefore, multi-task learning is employed in this work. To further improve the inversion accuracy, a hybrid network leveraging the advantages of Fully Convolutional Residual Network (FCRN) and Gated Recurrent Unit Network (GRU) is proposed for the simultaneous inversion of the velocity of P-wave and density, named Multi-task FCRN and GRU (MFG). FCRN is responsible for the efficient extraction of local information from the seismic data, while GRU captures the global dependencies in the data along time. To be mentioned, an auxiliary task of seismic data reconstruction has been added as a regularization technique to enhance the stability of network training. The experimental results obtained using both synthetic model and field data indicate that MFG significantly enhances the inversion accuracy, lateral continuity, and vertical resolution.