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.

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.

Ground-based, electrical-source, and UAV-borne receiver TEM configurations have previously been used to map mines in Jiangsu Province, China. In this study, the EM responses of air-filled mine tunnels were simulated by using a three-dimensional (3D) vector finite element method. A new apparent resistivity formula has been proposed for the ground-airborne TEM configuration. In the study area, field measurements were carried out along 36 profiles for the ground-airborne TEM and 16 profiles for ground TEM. The ground-airborne TEM results were determined to be in good agreement with the ground TEM survey results using a surface central loop, and were also consistent with the known geologic conditions. The experiment was successful and showed that a ground-ground-airborne TEM method could potentially become a novel alternative for both future civilian and military applications.