###### Abstract  
Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion. fault detection; borehole data; electrical resistivity inversion; electrical resistivity tomography; deep learning; U-Net; initial model +
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utilizing electrical resistivity surveys in both terrestrial and marine environments [13; 14; 15; 16; 17]. These studies collectively demonstrate the wide-ranging applicability and effectiveness of electrical resistivity surveys in detecting and mapping fault structures. To analyze fault zones in electrical resistivity surveys, deterministic inversion methods are typically used. However, the traditional inversion process is challenging due to its ill-posed nature, which can result in non-unique and unstable solutions. In particular, if the initial model is far from the true subsurface properties, it can lead to convergence problems or inaccurate estimates. Additionally, the complexity of the numerical calculations involved makes the inversion process both time-consuming and computationally expensive [18; 19]. Recently, studies have actively incorporated deep-learning technology into ERT inversion to overcome its limitations. [PERSON] et al. [20] introduced ERSInvNet, which is based on the U-Net architecture, for mapping apparent resistivity data to resistivity models. They used a tier feature map as an additional input feature to mitigate the ambiguity that arises when using a convolutional neural network (CNN) due to the vertical variation in patterns between the input and output. [PERSON] et al. [21] also designed an adaptive CNN for electrical resistivity inversion, taking into account the vertically varying characteristics between apparent resistivity data and resistivity models. Similarly, [PERSON] and [PERSON] [22] proposed a deep-learning algorithm for the three-dimensional (3D) reconstruction of ERT, using SegNet architecture for the inversion network and training it with subsurface resistivity models generated by a geostatistical anisotropic Gaussian generator and corresponding apparent resistivity. [PERSON] et al. [23] also developed a ERT inversion using deep learning, proposing a variational encoder-decoder network for the inversion network and constructing realistic resistivity synthetic models with complex layers. Furthermore, various studies have applied similar approaches to electromagnetic (EM) inversion, with [PERSON] et al. [24] developing a CNN model to delineate salt dome structure from marine controlled-source EM (CSEM) data and other studies utilizing deep-learning technology for the inversion of airborne EM data [25; 26; 27].