## 1 Introduction  
The airborne time-domain electromagnetic (ATEM) is a geophysical exploration method that uses aircraft as an observation carrier. The method has several advantages, including the fact that it can be used over large observation areas, as well as its good terrain adaptability and high measurement efficiency. With the continuous improvement of instrument observation accuracy, data processing efficiency, and inversion interpretation resolution, the ATEM method has been widely used in mineral exploration [1; 2], groundwater investigation [3; 4], environmental monitoring [5], and geological mapping [6; 7]. The ATEM survey aims to explore the subsurface resistivity distribution through processing and interpretation of the measured data. Fast imaging and inversion methods are the main means of interpretation. Due to the fact that the ATEM method can be used over large observation areas, a typical ATEM survey involves sounding locations on the order of \(10^{4}\)-\(10^{7}\)[5]. Therefore, in practical engineering projects, fast interpretation methods are the most needed and the most effective. In recent decades, the main methods for the fast interpretation of ATEM data are fast imaging and inversion based on one-dimensional layered geological models. Fast imaging methods, such as conductivity-depth imaging (CDI) [8] and the EM Flow method [9; 10; 11],work by directly converting observed data into apparent resistivity and depth. Since fast imaging methods do not require an optimization algorithm, they can quickly obtain the subsurface resistivity distribution. However, the accuracy of such methods is low, and they are often used to build an initial model for fine inversion. Therefore, in order to obtain more accurate interpretation results, the ATEM data need to be inverted. Due to the large amount of ATEM data available, the one-dimensional inversion method is still the most widely used method for interpreting survey data. The conventional inversion methods involve deterministic inversion based on optimization, such as least-squares inversion [12], Occam inversion [13], and constrained inversion based on regularization techniques [14; 15; 16; 17; 18; 19; 20]. The results of these inversion methods are non-unique and greatly influenced by the initial model. A good initial model can speed up the convergence of the inversion and improve the accuracy of the inversion results. Eventually, a \"best\" inversion model within the expected error range is obtained. Although the conventional deterministic inversion method can obtain better inversion results, its computing cost can often not meet the needs of real-time data interpretation. As the application of ATEM exploration is becoming more and more prevalent, the rapid processing and interpretation of ATEM data are becoming very important, and it is expected that resistivity imaging results with better accuracy can be obtained in real time. An interpretation method with high precision and high speed is particularly needed. With the rapid development and popularity of artificial intelligence technology, deep learning methods based on convolutional neural network (CNN) structures can establish a relationship model between input and output through model training. Once the network structure is trained, output results can be quickly given for new input data. Therefore, an inversion method based on deep learning could provide an effective solution for the fast and real-time interpretation of ATEM data. In recent years, the application of deep learning technology in ATEM data processing has been the subject of some preliminary research. Firstly, deep learning algorithms can be applied to the forward simulation of the airborne electromagnetic method to find the model relationship between the geoelectric model and the observed electromagnetic response [21], so as to achieve fast forward simulation.