基于深度学习的子波整形反褶积方法

doi:10.13810/j.cnki.issn.1000-7210.2023.06.002

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Abstract Seismic data migration imaging is one of the important methods for estimating the reflectivity of underground media.However，the imaging results are often affected by the wavelet，with limited wavenumber band distribution.Effectively extending the wavenumber band of the imaging results to improve the spatial resolution is a key objective in broadband reflectivity estimation.To achieve this，we firstly point out that the wavelet and the illumination of the geometry system are two important factors that affect the resolution of imaging results from an inversion imaging perspective.Then，based on convolutional neural networks （CNN），we use broadband wavelets to construct labels and employ conventional imaging results as input features to explore the mapping relationship using CNN.We also develop a corresponding deep learning algorithm，namely the wavelet shaping deconvolution method，and design a solution to the problem of inaccurate initial wavelet estimation in deconvolution by concatenating，iterating，and updating wavelets and reflectivity.Customized broadband wavelets can take into account both low-wavenumber and high-wavenumber information and can better restore broadband reflectivity during network training.Finally，we use a known model for network pre-training，extract effective wavelets based on the target data as the initial wavelets for deconvolution of the target data，carry out wavelet shaping deconvolution processing，and test the correctness and reliability of the method through thin-layer model testing.The filed data processing results indicate that this method has great potential for practical applications.

Key words： image deconvolution wavelets convolutional neural networks deep learning

Received: 08 September 2022

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P631

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	Articles by authors
	Ni Wenjun
	Liu Shaoyong
	Wang Liping
	Han Bingkai
	Sheng Shen

Cite this article:

Ni Wenjun,Liu Shaoyong,Wang Liping等. Wavelet shaping deconvolution based on deep learning[J]. OGP, 2023, 58(6): 1313-1321.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.06.002 OR http://www.ogp-cn.com/EN/Y2023/V58/I6/1313

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