## 3 Experiment
### Evaluating the Performance of DL Inversion Models Using Synthetic Test Dataset  
After training the models in the three stages, the performance of each model was evaluated by comparing the results on the test datasets, which consisted of the layered, one-fault, and two-fault structures. The normalized root-mean-squared error (NRMSE), which is calculated by normalizing the root-mean-squared error (RMSE) by the difference between the maximum and minimum values, was used to quantify the performance of the trained inversion models. In Figure 11, the base model without a borehole mixer and borehole information shows promising results; however, the fine-tuning model shows the clearest and most similar results to the ground truth with the lowest NRMSE. Similarly, as shown in Figure 12, for a one-fault structure, all three models predicted the fault zone very clearly, but artifact noise is observed in the base model. The artifacts disappeared in borehole mixer training and fine-tuning, confirming results that incorporating the borehole information in the borehole mixer network helps predict the weather ground layer, which the base model cannot predict. To verify the generalization performance of the deep-learning model, prediction results were also compared for the two-fault structure, and the fine-tuning model exhibited the best performance, as shown in Figure 13. It is important to note that a two-fault structure was not included in the training dataset, which only consisted of a one-fault model. By evaluating the model's performance on an unseen fault configuration, we were able to gauge its ability to generalize beyond the specific training data. Moreover, a comparsion of the prediction results of the three types of test datarevealed that the fine-tuning model provided the highest prediction accuracy. Additionally, to evaluate the effectiveness of the mixer network incorporating borehole information, we compared the results of the fine-tuning model with data from an increasing number of borehole data. As shown in Figure 14, as the number of boreholes increases, the NRMSE value gradually decreases with less artifact noise and clearer layered structures. Notably, when examining the red circles in Figure 14i-m, it is evident that an increase in the number of boreholes from zero to one, one to two, and two to four progressively enhanced the distinction of the three layers. Additionally, when there was no change in the borehole configuration within the red circles, the NRMSE showed a slight decrease. However, with changes in the borehole configuration, the NRMSE exhibited a significant decrease. These findings emphasize that both the number and distribution of boreholes play a crucial role in the inversion's performance. Figure 10: Loss curves for base model training, borehole mixer training, and fine-tuning of the DL-ERT inversion. Figure 11: Prediction results of DL-ERT inversion for the layered structure: (**a**) boreholes injected during training, (**b**) ground truth, (**c**) base model result: NRMSE = 0.0355, (**d**) borehole mixer training result: NRMSE = 0.0245, and (**e**) fine-tuning result: NRMSE = 0.0235. Figure 12: Prediction results of DL-ERT inversion for the one-fault structure: (**a**) boreholes injected during training, (**b**) ground truth, (**c**) base model result: NRMSE = 0.0343, (**d**) borehole mixer training result: NRMSE = 0.0272, and (**e**) fine-tuning result: NRMSE = 0.0217. Figure 13: Prediction results of DL-ERT inversion for the two-fault structure: (**a**) boreholes injected during training, (**b**) ground truth, (**c**) base model result: NRMSE = 0.0115, (**d**) borehole mixer training result: NRMSE = 0.0076, (**e**) fine-tuning result: NRMSE = 0.0049. Finally, we compared our results with those from conventional deterministic inversion methods. In this work, we used the conventional smoothing least-squares inversion method with SimPEG.