波动方程正演引导的深度学习地震波形反演

doi:10.13810/j.cnki.issn.1000-7210.2023.03.001

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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.

Key words： deep learning seismic inversion velocity-model building forward modeling wave equation

Received: 20 May 2022

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P631

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	Articles by authors
	DUAN Youxiang
	CUI Lele
	SUN Qifeng
	DU Qizhen

Cite this article:

DUAN Youxiang,CUI Lele,SUN Qifeng等. Deep learning seismic waveform inversion based on the forward modeling guidance of wave equation[J]. OGP, 2023, 58(3): 485-494.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.03.001 OR http://www.ogp-cn.com/EN/Y2023/V58/I3/485

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