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Time-domain airbe electromagnetic (AEM) data are frequently subject tointerference 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 leing for EM data denoising. This method uses dictionary leing to perform feature analysis and to extract and reconstruct the true signal. In the process of dictionary leing, the random noise is fi ltered out as residuals. To verify the effectiveness of this dictionary leing approach for denoising, we use a fi xed overcomplete discrete cosine transform (ODCT) dictionary algorithm, the method-of-optimal-directions (MOD) dictionary leing algorithm, and the K-singular value decomposition (K-SVD) dictionary leing 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.