Yile Ao

Professor Ao is a young reserve talent introduced by Beijing University of Chemical Technology in 2023. He graduated with a bachelor's degree in Exploration Technology and Engineering from China University of Petroleum in 2012, a master's degree in Geological Engineering from China University of Petroleum in 2015, and a doctoral degree in Geological Engineering and Geological Resources from China University of Petroleum in 2019. He worked as a postdoctoral fellow in the Department of Automation at Tsinghua University from 2019 to 2022, and has been working at the School of Mechanical and Electrical Engineering at Beijing University of Chemical Technology since 2023.

Contact Me: aoyile@yeah.net

Research Interests: 

1.  Machine Learning & Deep Learning Algorithms;

2.  Intelligent Image/Signal Processing Algorithms;

3.  Applications of Geophysical Data Processing, Interpretation;

4.  Applications of Mechanical Fault Detection and Status Monitoring;

Representative Papers: 

1. Ao Yile, Tian Fei, Lu Wenkai, et al. Improving Logging-While-Drilling Azimuthal Imaging With Deep Learning Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing, 2024, 63: 4500415. DOI:10.1109/TGRS.2024.3513640

2. Tian Xinyu, Ao Yile, Li Yanda, et al. A ground-roll separation method based on neural networks with morphological similarity loss[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 7505805. DOI: 10.1109/LGRS.2023.3317528

3. Gu Xiaofeng, Lu Wenkai, Ao Yile, et al. Seismic stratigraphic interpretation based on deep active learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-11. DOI: 10.1109/TGRS.2023.3288737

4. Wang Qi, Wang Yuqing, Ao Yile, et al. Seismic inversion based on 2D-CNNs and domain adaption[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5921512. DOI:10.1109/TGRS.2022.3213337

5. Ma Qiming, Wang Yuqing, Ao Yile, et al. UB-Net: Improved seismic inversion based on uncertainty backpropagation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5915211. DOI:10.1109/TGRS.2022.3174911

6. Ao Yile, Lu Wenkai, Xu Pengcheng, et al. Seismic dip estimation with a domain knowledge constrained transfer learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4501216. DOI:10.1109/TGRS.2021.3061438

7. Ao Yile, Lu Wenkai, Hou Qiuyuan, et al. Sequence-to-sequence borehole formation property prediction via multi-task deep networks with sparse core calibration[J]. Journal of Petroleum Science and Engineering, 2022, 208: 109637. DOI:0.1016/j.petrol.2021.109637

8. Ao Yile, Lu Wenkai, Jiang Bowu, et al. Seismic structural curvature volume extraction with convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9): 7370-7384. DOI:10.1109/TGRS.2020.3042098

9. Ao Yile, Lu Wenkai, Hou Qiuyuan, et al. Synthesize nuclear magnetic resonance T2 spectrum from conventional logging responses with spectrum regression forest[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(10): 1726-1730. DOI:10.1109/LGRS.2020.3008183

10. Ao Yile, Zhu Liping, Guo Shuang, et al. Probabilistic logging lithology characterization with random forest probability estimation[J]. Computers & Geosciences, 2020, 144: 104556. DOI:10.1016/J.CAGEO.2020.104556

11. Ao Yile, Li Hongqi, Zhu Liping, et al. A SCiForest based semi-supervised learning method for the seismic interpretation of channel sand-body[J]. Journal of Applied Geophysics, 2019, 167: 51-62. DOI:10.1016/J.JAPPGEO.2019.04.019

12. Li Xiuquan, Ao Yile, Guo Shuang, et al. Combining regression kriging with machine learning mapping for spatial variable estimation[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(1): 27-31. DOI:10.1109/LGRS.2019.2914934

13. Ao Yile, Li Hongqi, Zhu Liping, et al. Logging lithology discrimination in the prototype similarity space with random forest[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 16(5): 687-691. DOI:10.1109/LGRS.2018.2882123

14. Ao Yile, Li Hongqi, Zhu Liping, et al. Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm[J]. Journal of Petroleum Science and Engineering, 2019, 173: 781-792. DOI:10.1016/J.PETROL.2018.10.048

15. Ao Yile, Li Hongqi, Zhu Liping, et al. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling[J]. Journal of Petroleum Science and Engineering, 2019, 174: 776-789. DOI:10.1016/J.PETROL.2018.11.067 

Research Projects: 

1.       2025-Now: Compression Transmission and Key Frame Recognition Technology for Multi-parameters Monitoring Video Data in Fire Scene, Subproject of National Key Research and Development Program (2024YFC3016202), Subproject Leader.

2.       2024-2025: Development of Post-processing and Acquisition Software for Ultrasound Images on the Windows Platform, Enterprise Funded Project, Project Leader.

3.       2022-2023: Research on Surfaces Reconstruction based Intelligent Seismic Horizons Interpretation. NSF C Youth Project (42104117), Project Leader.

4.       2019-2024: Research on Artificial Intelligence based Multi-information Compatibility Expression. National Key Research and Development Program Project (2018YFA0702501), Key Participant.

5.       2019-2023: Logging Constrained Seismic Inversion based on Closed-loop Convolutional Neural Network. NSFC General Project (41974126), Key Participant.

6.       2016-2021: High Precision Parameter Identification Noise Suppression and Weak Signal Enhancement Technology. National Science and Technology Major Project (2016ZX05024-001-005), Project Participant.

1. Python Language Programming

2. Fundamentals of Artificial Intelligence

3. Artificial Intelligence and Applications

4. Neural Networks and Deep Learning