## 4 Conclusions  
In this study, a deep learning inversion method for ATEM data was constructed based on one-dimensional CNNs, and the effectiveness of this inversion method was examined  
Figure 8: The inversion results of Flight Line 5060 from 7960 km to 7980 km. (**a**) CNN inversion results; (**b**) regularization inversion results.  
Figure 7: The ATEM response data of Flight Line 5060 from 7960 km to 7980 km.  
using synthetic data and survey data. The results of the synthetic data experiments show that the size of the training dataset has a large impact on the training of the convolutional neural network structure, and with an increase in the number of training samples, both the training and validation RMSE values could be improved. However, it is also necessary to prevent the overfitting problem due to the excessive number of training samples. In addition, using a hybrid approach to generate the training sample dataset can improve the generalization ability of this CNN inversion, enabling it to cope with the inversion of ATEM data from a variety of complex geological situations. The results of our survey data tests show that the network structure of this CNN inversion can be adapted to ATEM data from different measurement systems without major adjustments, and it can obtain better inversion results for both simple and complex stratigraphic structures. Compared with the conventional one-dimensional regularized inversion method, the proposed CNN inversion method can obtain consistent inversion results and even better reconstruct the stratigraphic structure of regions with high-resistance layers. Further, for the simple layered structure, the CNN inversion results show clearer demarcation between the layers. More importantly, once the one-dimensional convolutional neural network structure of the corresponding survey area is obtained through training in the early stage, the conductivity-depth results can be predicted quickly, and the computation time required is much less than the conventional optimal inversion method. Therefore, the proposed CNN inversion strategy offers an effective and general means for the real-time interpretation of ATEM data, and it can also be extended to the interpretation of other geophysical data.  
Conceptualization, X.Y. ([PERSON]) and X.Y. ([PERSON]); data curation, X.Y. ([PERSON]) and P.Z.; formal analysis, P.Z. and X.Y. ([PERSON]); funding acquisition, [PERSON] ([PERSON]); investigation, X.Y. ([PERSON]) and P.Z.; methodology, [PERSON] ([PERSON]) and P.Z.; project administration, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); validation, P.Z.; visualization, P.Z.; writing--original draft, [PERSON] ([PERSON]) and P.Z.; writing--review and editing, P.Z. and X.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Sichuan Science and Technology Program, grant number 2023 NSFSC0778, and the Open Fund of Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, grant number MSSB-2022-11.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank the U.S. Geological Survey and Geoscience Australia for providing the survey data.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.