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基于改进环形生成对抗网络的浅地层剖面去噪方法

张一 丁仁伟 赵硕 孙世民 韩天娇

张一, 丁仁伟, 赵硕, 等. 基于改进环形生成对抗网络的浅地层剖面去噪方法[J]. CT理论与应用研究, 2023, 32(1): 1-11. DOI: 10.15953/j.ctta.2022.053
引用本文: 张一, 丁仁伟, 赵硕, 等. 基于改进环形生成对抗网络的浅地层剖面去噪方法[J]. CT理论与应用研究, 2023, 32(1): 1-11. DOI: 10.15953/j.ctta.2022.053
ZHANG Y, DING R W, ZHAO S, et al. Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network[J]. CT Theory and Applications, 2023, 32(1): 1-11. DOI: 10.15953/j.ctta.2022.053. (in Chinese)
Citation: ZHANG Y, DING R W, ZHAO S, et al. Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network[J]. CT Theory and Applications, 2023, 32(1): 1-11. DOI: 10.15953/j.ctta.2022.053. (in Chinese)

基于改进环形生成对抗网络的浅地层剖面去噪方法

doi: 10.15953/j.ctta.2022.053
详细信息
    作者简介:

    张一:女,山东科技大学地球科学与工程学院硕士研究生,主要从事人工智能地球物理应用研究,E-mail:minniez423@163.com

    丁仁伟:男,博士,山东科技大学地球科学与工程学院讲师,主要从事地球信息科学与技术、高性能计算、地震成像与速度建模理论与方法、地震数据智能处理与解释等方面研究,E-mail:dingrenwei@126.com

    通讯作者:

    男,博士,山东科技大学地球科学与工程学院讲师,主要从事地球信息科学与技术、高性能计算、地震成像与速度建模理论与方法,地震数据智能处理与解释等方面研究,E-mail:dingrenwei@126.com

  • 中图分类号: O  242;P  315;P  631

Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network

  • 摘要: 为解决浅地层剖面数据噪声多、分辨率低问题,本文将环形生成对抗网络的方法应用于浅地层剖面资料的去噪,实现智能去噪。首先,选择具有特殊对称生成对抗网络循环机制以及“循环一致性”损失的环形生成对抗性网络,并对其进行结构改进,提升网络学习和训练的性能。然后,基于优化的浅地层剖面样本集训练网络,实现对于浅地层剖面数据随机噪声的去除,提升数据的信噪比。通过对实验和实际资料的试算,以及与传统带通滤波方法的对比,验证本文方法对浅地层剖面数据去噪的有效性和适应性。

     

  • 图  1  浅地层剖面原理图

    Figure  1.  Schematic diagram of shallow formation profile

    图  2  GAN结构

    Figure  2.  Structure of GAN

    图  3  环形生成对抗网络CycleGAN结构

    Figure  3.  Structure of CycleGAN

    图  4  CycleGAN生成器网络结构

    Figure  4.  Structure of generator network

    图  5  CycleGAN判别器网络结构

    Figure  5.  Structure of discriminator netwok

    图  6  循环一致性损失

    Figure  6.  Cycle-consistency loss

    图  7  CycleGAN的网络模型去噪流程

    Figure  7.  Denoising process based on CycleGAN

    图  8  CycleGAN训练结果

    Figure  8.  Cyclegan training results

    图  9  CycleGAN训练结果

    Figure  9.  Cyclegan training results

    图  10  真实数据去噪效果

    Figure  10.  Actual shallow profile data denoising effec

    图  11  实际数据带通滤波效果

    Figure  11.  Actual data bandpass filtering effect

    表  1  两种去噪方法PSNR对比

    Table  1.   PSNR comparison of two denoising methods

    去噪方法PSNR/dB
    带通滤波去噪18.729
    CycleGAN去噪25.247
    下载: 导出CSV
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  • 收稿日期:  2022-03-27
  • 修回日期:  2022-04-06
  • 录用日期:  2022-04-19
  • 网络出版日期:  2022-04-29

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