师资队伍

敖亦乐 副教授

发布者：siqiJ发布时间：2026-03-23浏览次数：10


敖亦乐·博士·副教授

通讯地址：北京化工大学机电工程学院，联系方式：aoyile@yeah.net

个人概况：

北京化工大学2023年引进青年后备人才，硕士生导师。主要研究方向包括智能图像处理与感知算法、地球物理数据处理与解释算法以及机械状态监测与感知方法。现已发表 SCI/EI 检索论文20余篇。主持自然科学基金青年基金项目一项、国家重点研发子课题一项及多项横向科研课题，并作为科研骨干参与国家重点研发计划、国家科技重大专项、自然科学基金面上项目等多项国家纵向项目。

教育背景：

2008.09-2012.06中国石油大学(华东) 学士

2012.09-2015.06中国石油大学(华东) 硕士

2015.09-2019.09中国石油大学(北京) 博士

工作履历：

2023.03-至今 北京化工大学 副教授

2019.11-2022.11 清华大学 博士后

教学工作：

主讲《Python 语言程序设计》、《人工智能基础》、《人工智能及应用》、《神经网络与深度学习》等本科生课程。

论文与专利：

  1. Improving logging-while-drilling azimuthal imaging with deep learning super-resolution. IEEE Transactions on Geoscience & Remote Sensing, 2024. DOI:10.1109/TGRS.2024.3513640

  2. A ground-roll separation method based on neural networks with morphological similarity loss. IEEE Geoscience & Remote Sensing Letters, 2023. DOI: 10.1109/LGRS.2023.3317528

  3. Seismic stratigraphic interpretation based on deep active learning. IEEE Transactions on Geoscience & Remote Sensing, 2023. DOI: 10.1109/TGRS.2023.3288737

  4. Seismic inversion based on 2D-CNNs and domain adaption. IEEE Transactions on Geoscience & Remote Sensing, 2022. DOI:10.1109/TGRS.2022.3213337

  5. UB-Net: Improved seismic inversion based on uncertainty backpropagation. IEEE Transactions on Geoscience & Remote Sensing, 2022. DOI:10.1109/TGRS.2022.3174911

  6. Seismic dip estimation with a domain knowledge constrained transfer learning approach. IEEE Transactions on Geoscience & Remote Sensing, 2022. DOI:10.1109/TGRS.2021.3061438

  7. Sequence-to-sequence borehole formation property prediction via multi-task deep networks with sparse core calibration. Journal of Petroleum Science & Engineering, 2022. DOI:0.1016/j.petrol.2021.109637

  8. Seismic structural curvature volume extraction with convolutional neural networks. IEEE Transactions on Geoscience & Remote Sensing. DOI:10.1109/TGRS.2020.3042098

  9. Synthesize nuclear magnetic resonance T2 spectrum from conventional logging responses with spectrum regression forest. IEEE Geoscience & Remote Sensing Letters, 2021. DOI:10.1109/LGRS.2020.3008183

  10. Probabilistic logging lithology characterization with random forest probability estimation. Computers & Geosciences, 2020. DOI:10.1016/J.CAGEO.2020.104556

  11. A SCiForest based semi-supervised learning method for the seismic interpretation of channel sand-body. Journal of Applied Geophysics, 2019. DOI:10.1016/J.JAPPGEO.2019.04.019

  12. Combining regression kriging with machine learning mapping for spatial variable estimation. IEEE Geoscience & Remote Sensing Letters, 2019. DOI:10.1109/LGRS.2019.2914934


  1. Logging lithology discrimination in the prototype similarity space with random forest. IEEE Geoscience & Remote Sensing Letters, 2018. DOI:10.1109/LGRS.2018.2882123

  2. Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm. Journal of Petroleum Science & Engineering, 2019, 173: 781-792. DOI:10.1016/J.PETROL.2018.10.048

  3. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science & Engineering, 2019. DOI:10.1016/J.PETROL.2018.11.067