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首页> 外文期刊>Geophysics: Journal of the Society of Exploration Geophysicists >The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network
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The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network

机译：基于小波神经网络的直升机传播瞬态电磁数据中的高频运动诱导噪声去除

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摘要

In helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could be classified as high-frequency motion-induced noise (HFM noise) based on its cause and time-frequency features, was observed in the field data of the Chinese Academy of Sciences-HTEM system. Because the HFM noise is an in-band noise for the HTEM response, it usually remains after the normal denoising procedure developed for the conventional motion-induced noise. To solve this problem, we have developed a three-stage workflow to remove the HFM noise using the wavelet neural network (WNN). In the first stage, the WNN training is performed, and the data segment in which the HFM noise is dominant is selected as the sample set. In the second stage, the HFM noise corresponding to the data segment in which the earth's response coexisted with the HFM noise is predicted using the well-trained WNN. In the last stage, the predicted HFM noise is removed from the corresponding original data. As an example, we applied our workflow in the field data observed in Inner-Mongolia, the HFM noise is removed effectively, and the results provide a strong data foundation for the subsequent processing procedures.
机译：在直升机传播的瞬态电磁（HTEM）信号处理中，移除运动诱导的噪声是最重要的步骤之一。在中国科学院 - HTEM系统的现场数据中，观察到基于其原因和时频特征的高频运动引起的噪声（HFM噪声）的特殊类型的短期噪声。 。因为HFM噪声是HTEM响应的带内噪声，所以它通常保持在为传统运动引起的噪声开发的正常去噪程序之后。为了解决这个问题，我们开发了一种三级工作流程，可以使用小波神经网络（Wnn）去除HFM噪声。在第一阶段，执行WNN训练，以及其中选择HFM噪声主导的数据段作为样本集。在第二阶段，使用训练有素的Wnn预测与其中与HFM噪声共存的地球响应共存的数据段对应的HFM噪声。在最后阶段，从相应的原始数据中移除预测的HFM噪声。作为一个例子，我们在内蒙古观察到的现场数据中应用了我们的工作流程，有效地去除了HFM噪声，结果为后续处理程序提供了强大的数据基础。

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