## 3 Inversion Experiments and Results
### Inversion of Survey Data  
To illustrate the applicability of the CNN inversion method proposed in this paper, we applied it to two types of ATEM survey data. The first type of data was collected from the Leach Lake Basin, Fort Irwin, California, using AeroTEM IV system, and the other data were collected from Gregory in Queensland, Australia, using VTEM(tm) system. These data are openly available and can be obtained from the U.S. Geological Survey website [29] and Geoscience Australia's website [30], respectively. The survey data extracted from the Leach Lake Basin were used to determine the distribution of groundwater resources, and it should be noted that the Leach Lake lies at the center of the basin. After data preprocessing, the electromagnetic response obtained using the AeroTEM IV system was converted into 17 channels of data between 58 us and 3.2 ms after current shutdown. In order to ensure that the CNN inversion method proposed in this paper can be applied to such survey data, we made a simple adjustment to the CNN structure. Specifically, firstly, we adjusted the input of the network model to a vector with a length value of 18 (18 \(\times\) 1) and formed the output of the network model into a vector with a length value of 20 (20 \(\times\) 1) via interpolation. Secondly, we adjusted the number of layers of the two-part convolution in the network structure to adapt to the new network model input and output requirements. In addition, we used the system parameters of AeroTEM IV to re-calculate the forward modelling of the 1 million forward geoelectric models used during the previous synthetic data experiments to obtain new training sample data. The Leach Lake Basin ATEM data contain 46 flight lines, each of which is about 16 km long. In this study, we selected Flight Line 10,430, which just crosses the Leach Lake, to illustrate the effectiveness of the CNN inversion method. The ATEM response data of Line 10,430 contain 6325 survey points, as shown in Figure 5. Figure 6 shows the results predicted using the one-dimensional CNN inversion algorithm and the results of the regularized inversion algorithm. As seen in Figure 6, the results obtained using the two inversion methods are consistent and show good agreement in the fault and low-resistivity anomalous regions. Of course, there are some differences between the two inversion results in some areas, especially in high-resistance areas. This is related to the sensitivity of the two inversion algorithms for high-resistance media inversion. Conventional regularization inversion often performs poorly in high-resistance regions, while CNN inversion performs well in both high-resistance and low-resistance regions. This has been verified in previous experiments with the same synthetic data described in this paper. In addition, in terms of time efficiency, the conventional one-dimensional regularized inversion takes 16,873 s [20], but prediction using the one-dimensional CNN inversion algorithm only takes about 22 s, which is a great improvement in time efficiency. Figure 5: The ATEM response data of Flight Line 10,430. According to the reference information provided by [PERSON] et al. [29], Flight Line 10,430 passes through the Granite Mountains, Desert King Spring Fault, Garlock Fault, and Leach Lake, the stratigraphic structures of which are relatively complex. However, the CNN inversion can be well inverted, which indicates that the CNN inversion method is effective for working with complex stratigraphic structures. Therefore, once the one-dimensional CNN model of the corresponding measurement work area is obtained through training in the early stage, the conductivity-depth results of the survey data can be predicted quickly. This is very helpful for real-time interpretation of the distribution of the underground medium in practical observation. The survey data extracted from Gregory in Queensland, Australia, were acquired using the VTEM(tm) system, which has a large secondary field time range (about 10 ms) after the current is switched off.