Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

中文翻译：

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基于深度学习的机载电磁数据多源噪声消除

机载电磁（AEM）数据的现有噪声消除过程通常包括几个步骤，每个步骤都使用特定的方法来消除特定类型的噪声。为了提高AEM去噪的效率并减少操作员的主观判断对处理结果的影响，我们采用了一种基于去噪自动编码器（DAE）的深度学习方法，该方法可在单个处理步骤中去除多源噪声。AEM数据中最常见的噪声源，包括运动引起的噪声，附近或中等距离的Sferics噪声，电力线噪声和背景电磁噪声，将与大量的模拟响应相结合以构建训练集。训练集中的数据将用于训练深度学习DAE神经网络，以便该神经网络可以充分学习信号和噪声的各自特征，并进一步有效地将AEM响应信号（有用信号）与上述噪声区分开。使用该方法对现场数据进行处理，并将处理结果与使用传统方法获得的结果进行比较。对比测试表明，该方法有助于减少主观因素对数据结果质量的影响，并缩短整个AEM数据处理时间。并将处理结果与使用传统方法获得的结果进行比较。对比测试表明，该方法有助于减少主观因素对数据结果质量的影响，并缩短整个AEM数据处理时间。并将处理结果与使用传统方法获得的结果进行比较。对比测试表明，该方法有助于减少主观因素对数据结果质量的影响，并缩短整个AEM数据处理时间。