## 6 Conclusions  
The accurate identification of faults and fractures from 3D seismic data is essential for subsurface interpretation, mapping, and reservoir characterization. The effectiveness of fault identification using the CNN framework strongly depends on the expansiveness of the training dataset and the associated labeling. The synthetic samples can ensure the accuracy of the labels but might not cover all features encountered in the actual seismic data recorded in the field. Pre-training on real seismic images would require labeling a great number of images, which would have been a major undertaking for such a project. Opting for a pre-trained CNN model to predict and interpret seismic faults on the 3D seismic volume and using the UNET topology offered the most pragmatic approach. Faults identified by the pre-trained CNN were expressed better than those of manual interpretation guided by the variance attribute. The three seismic interpretation scenarios that were detailed above demonstrate that, although most of the major faults identified on CNN are seen on the variance cube, some minor faults are seen on one and not on the other. The automated fault identification using the pre-trained CNN on the seismic dataset significantly reduced the interpretation time when compared to that of the manual fault identification and would be the preferred approach in future interpretation projects.  
Conceptualization, I.B.; methodology, M.M.I. and I.B.; software, M.M.I., I.B., M.E. and O.D.A.; validation, I.B., M.E., S.E. and M.M.I.; formal analysis, M.E., S.E., I.B., N.A.S., I.B. and M.M.I.; investigation, M.E.; resources, M.E.; data curation, M.E.; writing--original draft preparation, M.M.I.; writing--review and editing, M.M.I., I.B., M.E., S.E., N.A.S. and O.D.A.; visualization, M.E. and I.B.; supervision, M.E.; project administration, M.E.; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported through the following project: \"Machine Learning Application in lithology and fluid properties Prediction utilizing seismic and well log data for optimal reservoir characterization\". The grant cost center: 015 LC0-420, grant type: YUTP-FRG 1/2022, awarded to [PERSON].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The associated datasets used in this study are open source from Geoscience Australia. All data are available at [[https://nopims.dmp.wa.gov.au/Nopims/](https://nopims.dmp.wa.gov.au/Nopims/)]([https://nopims.dmp.wa.gov.au/Nopims/](https://nopims.dmp.wa.gov.au/Nopims/)) accessed on 20 August 2022.  
**Acknowledgments:** The authors would like to especially thank PETERONAS for funding this project that's cost center is 01015 LC0-420 and the type of grant: YUTP-FRG 1/2022. We thank SLB for giving us a license of the Petrel software (Version 2020) that we used for seismic interpretation and visualization to the Center for Subsurface Seismic Imaging (CSI), University Technology PETRONAS, Malaysia. Our thanks also go to Geophysical Insights for donating the Paradise software that we used for deep learning-based fault prediction. Many thanks go to the anonymous editors and reviewers for providing insightful suggestions on improving the paper's quality.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the structure of the study; in acquiring, analyzing, or interpreting the data; in the composition of the manuscript; or in the decision to publish the results.