Low frequency continuation of seismic data based on physically constrained U-Net network
ZHANG Yan1, ZHOU Yifan1, SONG Liwei2, DONG Hongli3
1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 3. Institute of Artificial Intelligence Energy Research, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract：Due to the influence of source and acquisition technology，seismic exploration data often lack low-frequency information，which will have a great impact on the subsequent inversion and imaging processing. Most of the existing low-frequency continuation methods of seismic data are based on the distribution characteristics of time-domain data，which is easy to cause serious loss of frequency and phase information. In order to solve this problem，a U-Net depth learning network based on seismic wave physical parameter constraints is proposed to carry out low-frequency continuation of seismic data. Firstly，we use the theory to guide the idea of data to orga-nize samples and generate a large number of seismic data with different characteristics； Then，the improved U-Net model combined with residual jump connection is used to learn the nonlinear mapping of low-frequency components from medium and high-frequency seismic data； Finally，combined with the physical parameter constraints of seismic signal，the recovery effect of frequency and phase is strengthened. Experiments show that this method has a strong effect on low-frequency recovery of seismic data，and is superior to similar methods in frequency and phase maintenance. It has high practical value for improving the subsequent processing and interpretation accuracy.
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