Low-Frequency Extrapolation of Prestack Viscoacoustic Seismic Data Based on Dense Convolutional Network

Abstract

Low frequency information in seismic data can improve seismic resolution and imaging accuracy, enhance the quality of inversion, and play an essential role in imaging algorithms such as full-waveform inversion. Sufficiently low frequency data can avoid the cycle skipping phenomenon during full-waveform inversion. During seismic data processing, the protection and reconstruction for low frequency information are therefore of great importance. In this paper, we systematically investigate the extrapolation of pre-stack viscoacoustic seismic low frequency data using a dense convolutional network to effectively establish the nonlinear relationship between high and low frequency data, and realize the extrapolation and reconstruction of viscoacoustic 0-5 Hz low frequency data using 5-30 Hz high-frequency component. And the generalizability of the method for different influencing factors such as wavelets, noise, and models is analyzed using Marmousi2 velocity model forward data. It is demonstrated that the method has high robustness and can be applied to different situations, and the accuracy is higher than that of the traditional convolutional neural networks method. The feasibility of the low frequency extrapolation method based on dense convolutional network is also verified by synthetic data, physical experiment simulation data, and field data testing, and superior to the traditional convolutional neural networks method.