## 5 Conclusions  
We proposed an improved fault detection workflow consisting of directional edge detection, summation, and edge enhancement. Compared to the conventional fault detection workflow using seismic attributes, the proposed method is less cumbersome for controlling the parameters and does not require the selection of seismic attributes based on individual case. It mainly controls the results on the basis of a sequence of seismic attributes and displays consistent results regardless of the cases. In addition, the attributes used are smaller than those of the conventional method, and the proposed workflow requires lesser process time. Overall, we found that clustering effectively enables the extraction of individual fault surfaces. In this method, the fault surface extracted from the edge detection cube was converted to a sufficient number of points to describe the orientation of the fault layer. Subsequently, clustering was performed on the azimuth and dip using DBSCAN, and individual fault surfaces with high continuity could be extracted from complex entangled fault structures. The proposed workflow was applied to field data obtained from the Vincent oil field in Australia. The results showed that sharper and continuous edges could be detected with significant noise reduction compared to the conventional methods. Thus, we demonstrate that the proposed model is applicable to field data and is more effective for fault detection. The proposed workflow reduces the overall cycle of fault analysis and human-induced errors. This also reduces the effort and time spent during the manual interpretation of faults and allows the interpreter to further invest time in quality control of the extracted faults. Conceptualization, M.K. and N.-K.K. ; methodology, J.Y. and N.-K.K. ; investigation, B.-Y.K. ; data curation, J.Y. and M.K. ; writing--original draft preparation, J.Y. and B.-Y.K. All authors read and agreed to the published version of the manuscript. This research was funded by the Korea Institute of Geoscience and Mineral Resources, grant number GP2020-023. Not applicable. We wish to thank Joongmoo Byun and Rise Lab of Hanyang University for their support. The authors declare no conflict of interest. * _[PERSON] et al. (2004)_ [PERSON]; [PERSON]; [PERSON] Detection of potential fractures and small faults using seismic attributes. _Lead. Edge_**2004**, _23_, 903-906. [CrossRef]
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