# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 6 Conclusions  
In conclusion, this research successfully detected geological features in seismic data automatically using a semi-supervised learning technique. We combine unsupervised learning to generate the label data using a proposed workflow and supervised learning to build a model for geological feature detection.  
The main problem of applying deep learning-based techniques to geoscience problems is the lack of label data for the training process, since it is a subjective and time-consuming task. To address this problem, we used the proposed workflow to generate numerous label datasets for training the model. This workflow starts with seismic attribute extraction to help enhance the visualization of the targeted geological features. Then, we applied KernelPCA to transform those multi-attributes into single principal components, and finally, we used K-means clustering to classify the geological label into binary classes.  
We consider this geological detection one of the image segmentation problems. We used one of CNN's architectures, namely U-Net, to build the model. The U-Net architecture was originally proposed for 2D medical purposes. We modified this architecture to fit the geological detection problem by tuning the hyperparameters, including changing the architecture and applying the transfer learning technique.  
We apply this semi-supervised learning technique to synthetic and real seismic 3D datasets with vastly different geological settings to validate it. The synthetic data are from the SEAM dataset, which is a salt-dominated field, and the real data are from a case study from the Malay Basin, which is interpreted as a meandering fluvial reservoir. The application of two dataset examples demonstrated that the trained model is accurate and consistent with manual interpretations. With the model prediction result, we were able to automatically extract the 3D body of the geological target. Furthermore, this model could help the interpreter focus on the task that needs human expertise and help the model obtain better detection and prediction results.  
Conceptualization, H.P. and A.H.A.L.; methodology, H.P.; software, H.P.; validation, H.P. and A.H.A.L.; formal analysis, H.P. and A.H.A.L.; investigation, H.P. and A.H.A.L.; data curation, H.P. and A.H.A.L.; writing--original draft preparation, H.P.; writing--review and editing, H.P. and A.H.A.L.; visualization, H.P.; supervision, A.H.A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the UTP 3D true-amplitude target-oriented migration (015 MD0-076).  
Not applicable.  
The SEAM synthetic datasets were analyzed in this study is publicly available datasets. This data can be found here: [[[https://seg.org/SEAM/open-data](https://seg.org/SEAM/open-data)]([https://seg.org/SEAM/open-data](https://seg.org/SEAM/open-data))].  
The authors would like to thank SEG Advanced Modeling for providing the open dataset. Also, PETRONAS Malaysia and the Centre for Subsurface Imaging UTP for providing the data for this study. We would like to express our appreciation to the Centre for Subsurface Imaging and our colleagues at the Geoscience department of Universite Teknologi PETRONAS for supporting us throughout the project. Huge gratitude is extended to the UTP 3D true-amplitude target-oriented migration project with cost center 015 MD0-076 for granting this research. We acknowledge the Schlumberger Company for providing Petrel software licensing.  
The authors declare no conflict of interest.