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首页> 外文期刊>Nonlinear processes in geophysics >Denoising stacked autoencoders for transient electromagnetic signal denoising
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Denoising stacked autoencoders for transient electromagnetic signal denoising

机译：用于瞬态电磁信号去噪的堆积堆叠自动化器

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摘要

The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFSDSA maps the signal points of the noise interference to the high-probability points with a clean signal as reference according to the deep characteristics of the signal, so as to realize the signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.
机译：瞬态电磁法（TEM）在地球物理中非常重要。然而，由线圈接收的TEM中的次级场信号（SFS）容易受随机噪声，传感器噪声和人造噪声的干扰，这导致难以检测深层地质信息。为了减少噪声干扰并检测深层地质信息，我们应用自动化器，这在深度学习中构成了一个无监督的学习模型，基于SFS的特征来表示SFS的特性。我们介绍了基于特征提取和去噪的深神经网络的SFSDSA（次级现场信号去噪堆叠AutoEncoders）模型。 SFSDSA根据信号的深度特性，使用清洁信号将噪声干扰的信号点映射到高概率点，以参考信号，以实现信号去噪并降低噪声干扰。该方法通过测量的数据比较验证，比较结果表明，降噪方法可以（i）与卡尔曼，主成分分析（PCA）和小波变换方法以及（II）有效地降低SFS的噪声。（II ）强烈支持猜测更深层次的地下特征。

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  • 来源
    《Nonlinear processes in geophysics》 |2019年第1期|共11页
  • 作者

    Lin Fanqiang; Chen Kecheng; Wang Xuben; Cao Hui; Chen Danlei; Chen Fanzeng;

  • 作者单位

    Chengdu Univ Technol Sch Informat Sci &

    Technol Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol Sch Informat Sci &

    Technol Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol Coll Geophys Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol Coll Geophys Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol Sch Informat Sci &

    Technol Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol Sch Informat Sci &

    Technol Chengdu 610059 Sichuan Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地球物理学;
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