A Feature Enhanced Autoencoder Integrated With Fourier Neural Operator for Intelligent Elastic Wavefield Modeling

Abstract

Seismic forward modeling plays a crucial role in Earth science, particularly in seismic exploration. It is essential for seismic data acquisition, inversion, and interpretation. Traditional numerical simulation methods necessitate grid partitioning of the computational domain and discrete approximations of time and space derivatives, which can lead to numerical dispersion and algorithmic instability. In recent years, the application of data-driven methods in seismic simulations has attracted significant attention and is expected to provide an effective alternative to traditional approaches. These methods such as neural operators (NOs), notably Fourier NO (FNO), enable rapid computations and reduce the time needed for resimulation due to changes in source and model parameters. However, in complex models, a single FNO struggles to accurately learn the wavefield solution. To enhance the accuracy and generalization performance of FNO in learning seismic wavefield information in complex geological models, we propose a novel model called multiscale feature extraction and aggregation embedded with Fourier neural operator deep network (MFEAFNet) for intelligent elastic wavefield modeling. Our proposed method integrates the feature extraction modules of the multiaxis feature extraction (MAFE), the convolutional block attention module (CBAM), and the multiscale cross-feature aggregation (MCFA) with FNO, allowing these modules to automatically learn the most representative features for wavefield data and, thereby, ensuring that the combined features are effectively delivered to the FNO module for better learning of fine details of complex elastic wavefields. Numerical experiments demonstrate that our method has high accuracy in predicting wavefields across different medium models and source locations and in long-term predictions.