## 4 Field Data Example  
In order to verify the reasonability of the proposed algorithm, a field test was conducted. The Chengba great fault is located in the Bashan-Chengkou-Fangxian area of the South Dabashan mountains, also known as the \"Chengkou-Fangxian deep fault\" or \"Dabashan Mountain deep fault\". The northwestern terminus of the Chengba fault commences in Xixiang, Shaanxi province, intersecting with the Yangpingguan-Yangxian deep fault. On its eastern end, the Chengba fracture extends into Hubei province and intersects with the Xiangfan-Guangji fault within Fangxian county. The Chengba fault has a general arc distribution, and its distribution pattern is prominent to the southwest. Spanning over 300 km in length, this fault serves as a tectonic boundary between the Yangtze plate and the South Qinling plate. In the Chengkou area, the Chengba fault zone exhibits a generally undulating plane, with the fault plane predominantly oriented due north, dipping at an angle between 40\({}^{\circ}\) and 80\({}^{\circ}\). The layout of the electric source and test line are designed as shown in Figure 14. In the test, the transmitter equipment is the ground-airborne electromagnetic transmission system developed by Jilin University with a maximum transmitting power of 200 kW. The received part used an unmanned aerial vehicle to load the lightweight receiver system. The flight speed is about 6 m/s and, during the apparent resistivity imaging, we acquire a point every 2 s, so the station spacing is around 12 m, then we obtain data with a lateral resolution of 12 m after data processing. The margin of error in data interpretation does not exceed 5%. Figure 14: Layout of the source (in red) and the receiving positions (in blue). The proposed NGO-LSSVM algorithm is used to interpret the measured data. According to the previous analysis, the variation in apparent resistivity of the conductive anomaly body is positive, indicating that as the resistivity decreases, the variation in apparent resistivity will reach a positive maximum value. The resistivity, however, exhibits a decrease in the presence of low resistance anomalies. Therefore, its trend of resistivity variation should be opposite to that of the resistivity. To compare the results of the apparent resistivity calculated from magnetic field and the apparent resistivity variation predicted from magnetic field gradient, Figure 15a shows the calculated apparent resistivity results, and Figure 15b is the predicted apparent resistivity variation results. The red lines indicate the inferred location of the Chengba fault zone, and the magenta line indicates the drill hole. In Figure 15a, the indication of the fault location is not clear enough in the apparent resistivity imaging section, so further analysis of the apparent resistivity-height curves at the sites is needed to identify the fault definitively [36]. However, in Figure 15b, the apparent resistivity variation imaging section reveals a distinct conductive area at the location of the fault, as depicted by the black dotted line, thereby enhancing the indication ability of the imaging sections. In addition, there is a suspected low-resistance region on the right side of the fault zone. To provide a clearer depiction of the distribution of apparent resistivity variation and facilitate a comparative analysis with the measuring of resistivity in the geological drilling method, Figure 16 illustrates the contrasting curves between the measured resistivity and predicted apparent resistivity variation at drill hole DW-01. Figure 15: Comparison between apparent resistivity and apparent resistivity variation. **(a)** Apparent resistivity-depth section calculated from magnetic field \(H_{\mathrm{z}}\), **(b)** apparent resistivity variation-depth section predicted from magnetic field gradient \(H_{\mathrm{z}}^{\Delta x}\). The measured resistivity curve at DW-01 by the geological drilling method is shown in Figure 16a, and the predicted resistivity variation curve by the NGO-LSSVM model is shown in Figure 16b.