## 3 Experiment  
### Data Generation Using Borehole Information  
To generate target-oriented subsurface resistivity models, we used borehole data acquired from 13 drilling sites in South Korea, including Jangheung, Jangseong, Gwangyang, and Suncheon in South Jeolla Province; Changwon, Yangsan, and Hadong in South Gyeongsang Province; Gwangju and Icheon in Gyeonggi Province; Jecheon in North Chungcheong Province; and Gijang-gun in Busan. Figure 9 shows a map indicating the locations of the drilling sites. Each site is composed of various rock types, including gneiss, schist, phyllite, acidic dike, granite, andesite, granite, phyllite, tuff, limestone, and metamorphic sedimentary rocks originating from a wide range of geological eras from the Precambrian to the Mesozoic. Each site includes 5-19 borehole data, for a total of 150 borehole data used in this study. Each borehole data provides information on the Transverse Mercator (TM) coordinates, depth (m), Unified Soil Classification System (USCS), and stratum composition. On the basis of the electrical resistivity values of the strata, we categorized them into four layers: soil layer, weather ground layer, basement rock layer, and fault layer, as determined by USCS. The range of electrical resistivity values for each stratum was set as follows: 500-1000 ohm-m for the soil layer, 500-2000 ohm-m for the weather ground layer, 1000-3000 ohm-m for the basement rock layer, and 200-500 ohm-m for the faults [36; 37]. It is important to note that there may be some overlap in the resistivity values between different strata. This is primarily due to the presence of mixed rock types within the field borehole data, which cannot be easily categorized into a single specific stratum. Then, the borehole information, including the coordinates, depth, and resistivity values, was used to generate resistivity models, with resistivity values randomly selected from within the specified range.  
Figure 8: Flowchart of the deterministic inversion with the initial model from the DL-ERT inversion.  
Employing the borehole information described above, we generated 3D resistivity models using GemPy. The 3D model was set to 200 \(\times\) 10 \(\times\) 200 m in the x, y, and z directions, respectively, and composed of three layers, namely, the soil layer, weathered ground layer, and basement layer, according to the field borehole data. We then added the fault layer to the layered model with a randomly selected dip angle ranging from 45\({}^{\circ}\) to 90\({}^{\circ}\) and thickness ranging from 20 to 100 m.  
For the forward modeling using SimPEG, we set the survey as a dipole-dipole array with a range of \(-\)320-320 m. The spacing between the electrodes was set to 20 m, and the maximum number of repetitions of the receiver for each transmitter was limited to 10.  
In this way, we generated 5000 sets of synthetic data, including layered models with and without a fault structure, and split them into training, validation, and test data sets with a 7:2:1 ratio. It is important to note that only one fault layer was added for the fault model at this stage; however, we also set additional test data with two fault layers to evaluate the generalization capability of the inversion model.