###### Abstract  
Faults represent important analytical targets for the identification of perceptual ground motions and associated seismic hazards. In particular, during oil production, important data such as the path and flow rate of fluid flows can be obtained from information on fault location and their connectivity. Seismic attributes are conventional methods used for fault detection, whereby information obtained from seismic data are analyzed using various property processing methods. The analyzed data eventually provide information on fault properties and imaging of fault surfaces. In this study, we propose an efficient workflow for fault detection and extraction of requisite information to construct a fault surface model using 3D seismic cubes. This workflow not only improves the ability to detect faults but also distinguishes the edges of a fault more clearly, even with the application of fewer attributes compared to conventional workflows. Thus, the computing time of attribute processing is reduced, and fault surface cubes are generated more rapidly. In addition, the reduction in input variables reduces the effect of the interpreter's subjective intervention on the results. Furthermore, the clustering method can be applied to the azimuth and dip of the fault to be extracted from the complexly intertwined fault faces and subsequently imaged. The application of the proposed workflow to field data obtained from the Vincentian oil field in Australia resulted in a significant reduction in noise compared to conventional methods. It also led to clearer and continuous edge detection and extraction. directional fault detection; fault extraction; seismic attributes; ant tracking; edge evidence; orientation clustering +
Footnote †: journal:with significant variation in interpretation according to the interpreter's experience [16; 17; 18; 19; 20]. Therefore, several technologies and tools have been developed to reduce the time taken for data processing of raw seismic data and minimize the impact of the interpreters [21]. The coherence measurement method used for geometric and hierarchical discontinuous surface analysis was proposed by [PERSON] and [PERSON] [14]. Subsequently, several such studies were performed based on different parameters such as semblance [22], eigenstructure [23; 24], gradient structure tensor [25], similarity [10; 26], variance [27], automated fault extraction [28], and directional structure-tensor coherence [29]. However, these methods are typically vulnerable to noisy data, which is problematic. Image processing is the task of improving and extracting useful information from a given source. In seismic studies, amplitude images are primarily used, which are applied to fault detection through various attributes. Edge detection has been put forward to effectively locate discontinuities using differences between seismic traces [30]. Attributes such as ant tracking [31] and Hough transform [32] have been proposed for edge detection and enhancement. A conventional fault-detection workflow using the aforementioned techniques is shown in Figure 1. A widely used method based on seismic attributes consists of the following parts: conditioning, edge detection, and edge enhancement. However, each part has several types of seismic characteristic parameters, whose selection depends on the researcher's subjective experience. As a result, differences in results are inevitable. Furthermore, obtaining a clear fault extraction output is difficult owing to noisy data. In addition, the extraction of individual faults from the cube requires intense manual work and might lead to human errors. In this study, we propose an effective workflow for fault detection and extraction to construct a fault surface model. This approach improves the workflow, enables faster and clearer fault detection, and leads to enhancement of the edges. In addition, individual fault surfaces can be extracted more easily by applying a clustering method to fault cubes.