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.