## 4 Discussion  
In this study, we propose a methodology that utilizes field borehole data to create target-oriented resistivity models. However, it is important to acknowledge that the resistivity models generated in this study are relatively simple, typically comprising of two or three layers with linear faults. This simplicity arises from the nature of the acquired field borehole data, which exhibit a relatively simple lithology characterized by a few distinct layers. Nonetheless, we believe that our proposed workflow can be extended to create more complex resistivity models by incorporating field borehole data from lithologically diverse and complex regions. Despite the promising results obtained from our methodology, several limitations warrant further attention. Firstly, our current deep-learning model is designed for 2D resistivity models and 2D survey data, lacking the inclusion of 3D effects that are crucial for accurately inverting field data. Incorporating three-dimensional considerations into our model will enhance its ability to capture the true complexity of subsurface resistivity distributions. Secondly, topographical information plays a vital role in the modeling and inversion processes. However, our current deep-learning model does not account for topographical effects. Future improvements to our methodology should focus on integrating topographical data to enhance the accuracy and reliability of the inversion results. Thirdly, when generating resistivity models with faults, we only considered a single-fault structure with a linear shape. Although this approach provides valuable insights, it may not fully capture the diverse range of fault structures encountered in real-world scenarios. Expanding the fault modeling capability of our methodology to incorporate various fault geometries will be an important area of future investigation. Additionally, it is worth noting that the training data used in this study were generated without considering noise. Including noise in the training data would enable the deep-learning model to better handle real-world data characterized by various noise sources and levels. Furthermore, for the training data, we intentionally used a wide electrode spacing of 20 m. This choice was made because a smaller electrode spacing would require more data points in the apparent resistivity map to cover the same survey area, resulting in a larger input and output size for the deep-learning model and increased computational memory requirements. The electrode spacing plays a crucial role in the sensitivity of detecting faults. In our study, we considered a fault thickness ranging from 20 to 100 m, taking into account the electrode spacing of 20 m. Therefore, to detect thin faults, a shorter electrode spacing and larger input and output data sizes for DL-ERT inversion need to be considered. Lastly, our current DL-ERT inversion model has been specifically trained for the dipole-dipole array configuration. Consequently, applying the model to other electrode configurations may yield suboptimal results due to inherent differences in data characteristics. We conducted an experiment comparing the performance of our model with Wenner, Schlumberger, and dipole-dipole arrays (Figure 18), confirming that our current DL-ERT model is not well-suited for non-dipole-dipole arrays. Addressing this limitation and developing a more versatile model capable of accommodating different electrode configurations will be the focal point of our future research. In our field data example, even though the comparison of the convergence curves between deterministic inversion with and without the DL-ERT initial model implies the effectiveness of our proposed workflow, it is worth mentioning that to fully demonstrate the efficacy of our approach, it would be beneficial to have borehole data for a direct comparison of the results. In summary, while our proposed methodology has demonstrated promising results, it is important to acknowledge and address the limitations outlined above.