Deep learning seismic waveform inversion based on the forward modeling guidance of wave equation
DUAN Youxiang1, CUI Lele1, SUN Qifeng1, DU Qizhen2
1. College of Computer Science and Technology, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. School of Earth Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China
Abstract:Physics-driven full-waveform inversion method has high computational overhead, while data-driven deep learning inversion method has a high dependence on the marker dataset. In order to obtain better inversion results with limited data, a deep learning seismic waveform inversion method based on the forward modeling guidance of the wave equation is proposed, which integrates data-driven and physics-driven methods. Firstly, a neural network is applied to reconstruct the velocity model based on seismic data, and the velocity model predicted by the network is modeled by forward simulation. Then the network is trained by minimizing the error of the velocity model and seismic data. Secondly, a finite difference method is adopted to approximate the second-order partial differential wave equation as a differentiable operator. The forward modeling process enables gradient transfer, and the weights of seismic data loss are dynamically adjusted according to the direction of the gradients. The experimental results show that the method can reduce the dependence of the data-driven method on the marker dataset to a certain extent, obtain more accurate velocity models, and have stronger robustness.
段友祥, 崔乐乐, 孙歧峰, 杜启振. 波动方程正演引导的深度学习地震波形反演[J]. 石油地球物理勘探, 2023, 58(3): 485-494.
DUAN Youxiang, CUI Lele, SUN Qifeng, DU Qizhen. Deep learning seismic waveform inversion based on the forward modeling guidance of wave equation. Oil Geophysical Prospecting, 2023, 58(3): 485-494.
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