## 5 Conclusions  
In order to convert the magnetic gradient data with high recognition ability to underground anomalies into electrical parameters for better data interpretation, the method of calculating the apparent resistivity variation from magnetic field gradient was studied in this paper. Firstly, we investigated the relationship between the magnetic field gradients and the earth's resistivity and found that each magnetic field gradient corresponds to a specific resistivity value at a certain measuring point, which enabled us to use the iteration-matching algorithm to solve the apparent resistivity. However, by calculating the apparent resistivity of uniform half-space models with different resistivities, it was found that random artificial anomalies exist, called background resistivity. Secondly, to mitigate the presence of random artificial anomalies, we employ LSSVM to obtain the apparent resistivity variation, eliminating the effect of background resistivity. Thirdly, to enhance the accuracy and avoid premature convergence to locally optimal solutions of LSSVM, the NGO algorithm was introduced to optimize the regularization parameter \(\gamma\) and the kernel parameter \(\sigma\). Finally, to verify the predictive capability of the NGO-LSSVM algorithm, three theoretical models were designed. The apparent resistivity variation predicted from the magnetic field gradient by the NGO-LSSVM algorithm in this paper was significantly better than the apparent resistivity calculated from the traditional magnetic field; a field test was conducted, and the imaging results demonstrated a clear depiction of the Chengba fault zone through apparent resistivity variation, and the borehole resistivity variation was in good agreement with the measured results of the geological drilling method. Although the apparent resistivity variation calculated from magnetic field gradient through the NGO-LSSVM algorithm can indicate the location of the anomalous body, further research is essential to develop combined imaging with traditional apparent resistivity to obtain the apparent resistivity closer to the real situation. The range of underground electrical parameters can be initially obtained through the calculation results of traditional apparent  
Figure 16: Comparison of resistivity from geological drilling method and predicted apparent resistivity variation by NGO-LSSVM algorithm: (**a**) resistivity curve of drill hole DW-01, (**b**) apparent resistivity variation curve of drill hole DW-01.  
resistivity. Then, within this range of apparent resistivity, the design model is refined to enrich the amount of data involved in the training network of the apparent resistivity variation, so that the prediction results of the apparent resistivity variation are more reasonable, and more accurate apparent resistivity sections results can be obtained after combining with the apparent resistivity.  
Conceptualization, C.L. and J.L.; methodology, J.L.; software, J.L.; validation, J.L. and S.L.; resources, C.L. and H.Z.; data curation, H.Z.; writing--original draft preparation, J.L.; writing--review and editing, C.L., J.L., S.L. and H.Z.; visualization, J.L. and S.L.; supervision, H.Z.; project administration, C.L.; funding acquisition, C.L. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Jilin Department (grant number 20220201102 GX) and the National Natural Science Foundation of China (grant number 42074152).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank all members in the EM group of the Key Lab of Geo-Exploration Instrumentation, Ministry of Education of China, who provided assistance for the field test.  
The authors declare no conflicts of interest.