Time-domain airborne electromagnetic (AEM) data are frequently subject to interference from various types of noise, which can reduce the data quality and affect data inversion and interpretation. Traditional denoising methods primarily deal with data directly, without analyzing the data in detail; thus, the results are not always satisfactory. In this paper, we propose a method based on dictionary learning for EM data denoising. This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal. In the process of dictionary learning, the random noise is filtered out as residuals. To verily the effectiveness of this dictionary learning approach for denoising, we use a fixed overcomplete discrete cosine transform (ODCT) dictionary algorithm, the method-of-optimal-directions (MOD) dictionary learning algorithm, and the K-singular value decomposition (K-SVD) dictionary learning algorithm to denoise decay curves at single points and to denoise profile data for different time channels in time-domain AEM. The results show obvious differences among the three dictionaries for denoising AEM data, with the K-SVD dictionary achieving the best performance.

时域机载电磁（AEM）数据经常受到各种类型噪声的干扰，这会降低数据质量并影响数据反演和解释。传统的去噪方法主要是直接处理数据，而没有详细分析数据。因此，结果并不总是令人满意的。本文提出了一种基于字典学习的EM数据降噪方法。该方法使用字典学习来执行特征分析并提取和重建真实信号。在字典学习的过程中，随机噪声被滤除为残差。为了切实地使用这种字典学习方法进行降噪，我们使用了固定的不完全离散余弦变换（ODCT）字典算法，最佳方向方法（MOD）字典学习算法和K奇异值分解（K-SVD）字典学习算法，以对单个点上的衰减曲线进行去噪并在时域中对不同时间通道的轮廓数据进行去噪AEM。结果表明，在用于去噪AEM数据的三个字典之间存在明显差异，其中K-SVD字典实现了最佳性能。