石油地球物理勘探2023，Vol.58Issue(6)：1313-1321,9.DOI:10.13810/j.cnki.issn.1000-7210.2023.06.002
基于深度学习的子波整形反褶积方法
Wavelet shaping deconvolution based on deep learning
摘要
Abstract
Seismic data migration imaging is one of the important methods for estimating the reflectivity of under-ground media.However,the imaging results are often affected by the wavelet,with limited wavenumber band distri-bution.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 illumina-tion 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 up-dating 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 re-liability of the method through thin-layer model testing.The filed data processing results indicate that this method has great potential for practical applications.关键词
图像反褶积/子波/卷积神经网络/深度学习Key words
image deconvolution/wavelets/convolutional neural networks/deep learning分类
天文与地球科学引用本文复制引用
倪文军,刘少勇,王丽萍,韩冰凯,盛燊..基于深度学习的子波整形反褶积方法[J].石油地球物理勘探,2023,58(6):1313-1321,9.基金项目
本项研究受国家自然科学基金项目"基于非平稳滤波算子的最小二乘反射系数估计及宽带波阻抗成像"(41974125)和中石化地球物理重点实验室项目"数据驱动的像域地震数据高保真高分辨处理"(36750000-23-FW0399-0003)资助. （41974125）
