Page 155 - 《应用声学》2022年第1期

第 41 卷 第 1 期 Vol. 41, No. 1
2022 年 1 月 Journal of Applied Acoustics January, 2022

⋄ 研究报告 ⋄

利用U-Net网络增强骨密度全波形反演

谢辉武 杨 艳 †

(武汉大学物理科学与技术学院 武汉 430072)

摘要：该文设计了一种使用 U-Net 网络解决骨密度全波形反演的初值依赖、多解、病态等问题的方法。首先使
用逆时偏移成像，将其结果输入神经网络得到模型的预分布。将该分布作为全波形反演目标函数的约束，可以
使反演的结果更接近最优值，还可以减小反演对初始值的依赖。该文进行了一些模拟实验，得出该文的方法可
以改进全波形反演对初始值的依赖和容易陷入局部极值的问题。
关键词：骨密度；全波形反演；U-net
中图法分类号: TP391 文献标识码: A 文章编号: 1000-310X(2022)01-0151-07
DOI: 10.11684/j.issn.1000-310X.2022.01.018

Enhanced full waveform inversion with U-Net for bone mineral density
measurement

XIE Huiwu YANG Yan

(School of Physics and Technology, Wuhan University, Wuhan 430072, China)

Abstract: This paper designs a method to solve the initial value dependence, multiple solutions, and ill-
conditions of the full waveform inversion of bone mineral density with U-Net. First, migration imaging is
used, and the results are input to the neural network to obtain the pre-distribution of the model. Using this
distribution as the constraint of the objective function of the full waveform inversion can make the inversion
result closer to the optimal value and reduce the dependence on the initial value. In this paper, some simu-
lation experiments have been carried out, and it is concluded that the method in this paper can improve the
dependence of the full waveform inversion on the initial value and easily fall into the problem of local extreme
value.
Keywords: Bone mineral density; Full waveform inversion; U-net

2021-01-24 收稿; 2021-04-07 定稿
作者简介: 谢辉武 (1996– ), 男, 河南平顶山人, 硕士研究生, 研究方向: 全波形反演。
† 通信作者 E-mail: 862829996@qq.com