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PUBLICATIONS

  1. Di, H., N. Pham, and A. Abubakar, 2024, Estimating geotechnical parameters using semi-supervised learning: An example to the Dutch offshore wind farm zone: Geophysics, 89(1): WA85-WA93. (Link) (PDF)

  2. Di, H., W. Hu, A. Abubakar, P. Devarakota, W. Li, and Y. Li, 2024, Latest advancements in machine learning for geophysics – Introduction: Geophysics, 89(1): 1-4. (Link) (PDF)

  3. Waheed, U. B., H. Di, J. Sun, and D. Angus, 2024, Introduction to special issue on machine learning applications in geophysical exploration and monitoring: Geophysical Prospecting, 72(1): 3-6. (Link) (PDF)

  4. Di, H., and A. Abubakar, 2023, Automating seismic-well tie via self-supervised learning: Geophysical Prospecting, 71(4): 698-712. (Link) (PDF)

  5. Di, H., and A. Abubakar, 2022, Automated active learning in seismic image interpretation: The Leading Edge, 41(9): 628-635. (Link) (PDF)

  6. Abubakar, A., H. Di, A. Kaul, C. Li, Z. Li, V. Simoes, L. Truelove, and T. Zhao, 2022, Deep learning for end-to-end subsurface modeling and interpretation: An example from the Groningen gas field: The Leading Edge, 41(4): 259-267. (Link) (PDF)

  7. Di, H., Z. Li, and A. Abubakar, 2022, Using relative geologic time to constrain convolutional neural network-based seismic interpretation and property estimation: Geophysics, 87(2): IM25-IM35. (Link) (PDF)

  8. Di, H., and A. Abubakar, 2022, Estimating subsurface properties using a semisupervised neural network approach: Geophysics, 87(1): IM1-IM10. (Link) (PDF)

  9. Abriel, B., M. Araya-Polo, H. Di, S. Fomel, J. Lomask, P. Nivlet, J. Vamaraju, and B. Wallet, 2022, Introduction to special section: Automated approaches to interpretation: Interpretation, 10(2): Sci-Sci. (Link) (PDF)

  10. Shafiq, M. A., Z. Long, H. Di, and G. AlRegib, 2021, A novel attention model for salient structure detection in seismic volumes: Applied Computing and Intelligence, 1(1), 31-45. (Link) (PDF)

  11. Di, H., C. Li, S. Smith, Z. Li, and A. Abubakar, 2021, Imposing interpretational constraints on a seismic interpretation convolutional neural network: Geophysics, 86(3), IM63-IM71. (Link) (PDF)

  12. Di, H., A. Kaul, L. Truelove, W. Li, W. Hu, and A. Abubakar, 2021, Data Analytics and Machine Learning Hackathon 2021: A deep dive into the open-source data challenge for E&P: The Leading Edge, 40(1), 68-71. (Link) (PDF)

  13. Di, H., L. Truelove, C. Li, and A. Abubakar, 2020, Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand: The Leading Edge, 39(10), 727-733. (Link) (PDF)

  14. Di, H., Z. Li, H. Maniar, and A. Abubakar, 2020, Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow: Geophysics, 85(4), WA77-WA86. (Link) (PDF)

  15. Di, H., and G. AlRegib, 2020, A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels: Geophysical Prospecting, 68(2), 521-535. (Link) (PDF)

  16. Di, H., M. A. Shafiq, Z. Wang, and G. AlRegib, 2019, Improving seismic fault detection by super-attribute-based classification: Interpretation, 7(3), SE251-SE267. (Link) (PDF)

  17. Di, H., D. Gao, and G. AlRegib, 2019, Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification: Geophysical Journal International, 218(2), 1262-1275. (Link) (PDF)

  18. Di, H., and G. AlRegib, 2019, Semiautomatic fault/fracture interpretation based on seismic geometry analysis: Geophysical Prospecting, 67(5), 1379-1391. (Link) (PDF)

  19. Di, H., M. Alfarraj, and G. AlRegib, 2019, Three‐dimensional curvature analysis of seismic waveforms and its interpretational implications: Geophysical Prospecting, 67(2), 265-281. (Link) (PDF)

  20. Di, H., and G. AlRegib, 2019, Reflector dip estimates based on seismic waveform curvature/flexure analysis: Interpretation, 7(2), SC1-SC9. (Link) (PDF)

  21. Di, H., T. Zhao, V. Jayaram, X. Wu, L. Huang, G. AlRegib, J. Cao, M. Araya-Polo, S. Chopra, S. Al-Dossary, F. Li, E. Gloaguen, Y. Lin, A. Solberg, and H. Zeng, 2019, Introduction to special section: Machine learning in seismic data analysis: Interpretation, 7(3); Sci-SCii. (Link) (PDF)

  22. Wu, X., H. Zeng, H. Di, D. Gao, J. Gao, K. Marfurt, S. al Dossary, and G. Dorn, 2019, Introduction to special section: Seismic geometric attributes: Interpretation, 7(2): Sci-Sci. (Link) (PDF)

  23. Payam, K., T. Wilson, T. Carr, A. Kumar, R. Hammack, and H. Di, 2019, Integrating distributed acoustic sensing (DAS) and borehole 3C geophone array data to identify long-period long-duration seismic events during stimulation of a Marcellus shale gas reservoir: Interpretation, 7(1), SA1-SA10. (Link) (PDF)

  24. Di, H., M. A. Shafiq, and G. AlRegib, 2018, Multi-attribute k-means clustering for salt boundary delineation from 3D seismic data: Geophysical Journal International, 215(3), 1999-2007. (Link) (PDF)

  25. Di, H., D. Gao, and G. AlRegib, 2018, 3D dip vector-guided auto-tracking for weak seismic reflections: A new tool for shale reservoir visualization and interpretation: Interpretation, 6(4), SN47-SN56. (Link) (PDF)

  26. Long, Z., Y. Alaudah, M.A. Qureshi, Y. Hu, Z. Wang, M. Alfarraj, G. AlRegib, A. Amin, M. Deriche, S. Al-Dharrab, and H. Di, 2018, A comparative study of texture attributes for characterizing subsurface structures in seismic volumes: Interpretation, 6(4), T1055-T1066. (Link) (PDF)

  27. Shafiq, M. A., H. Di, and G. AlRegib, 2018, A novel approach for automated detection of listric faults within migrated seismic volumes: Journal of Applied Geophysics, 155, 94-101. (Link) (PDF)

  28. Wang, Z., H. Di, M. A. Shafiq, Y. Alaudah, and G. AlRegib, 2018, Successful leveraging of image processing and machine learning in seismic structural interpretation: A review: The Leading Edge, 37(6), 451-461. (Link) (PDF)

  29. AlRegib, H., M. Deriche, Z. Long, H. Di, Z. Wang, Y. Alaudah, M. A. Shafiq, M. Alfarraj, 2017, Subsurface Structure Analysis using Computational Interpretation and Learning: A Visual Signal Processing Perspective: IEEE Signal Processing Magazine, 35(2), 82-98. (Link) (PDF)

  30. Di, H., and D. Gao, 2017, Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation: Interpretation, 5(3), SJ31-SJ40. (Link) (PDF)

  31. Di, H., and D. Gao, 2017, 3D seismic flexure analysis for subsurface fault detection and fracture characterization: Pure and Applied Geophysics, 174(3), 747-761. (Link) (PDF)

  32. Huang, Y., H. Di, R. Malekian, X. Qi, and Z. Li, 2017, Noncontact measurement and detection of instantaneous seismic attributes based on complementary ensemble empirical mode decomposition: Energies, 10(10), 1655. (Link) (PDF)

  33. Di, H., and D. Gao, 2016, Improved estimates of seismic curvature and flexure based on 3D surface rotation in the presence of structure dip: Geophysics, 81(2), IM37-IM47. (Link) (PDF)

  34. Di, H., and D. Gao, 2016, Efficient volumetric extraction of most positive/negative curvature and flexure attributes for improved fracture characterization from 3D seismic data: Geophysical Prospecting, 64(6), 1454-1468. (Link) (PDF)

  35. Gao, D., and H. Di, 2015, Extreme curvature and extreme flexure analysis for fracture characterization from 3D seismic data: New analytical workflows and geologic implications: Geophysics, 80(2), IM11-IM20. (Link) (PDF)

  36. Di, H., and D. Gao, 2014, Gray-level transformation and Canny edge detection for 3D seismic discontinuity enhancement: Computers & Geosciences, 72, 192-200. (Link) (PDF)

  37. Di, H., and D. Gao, 2014, A new algorithm for evaluating 3D curvature and curvature gradient for improved fracture detection: Computers & Geosciences, 70, 15-25. (Link) (PDF)

  38. Zheng, Z., P. Kavousi, and H. Di, 2014, Multi-attribute and neural network-based fault detection in 3D seismic interpretation: Advanced Materials Research, 838-841, 1497-1502. (Link) (PDF)

  39. 邸海滨, 许力生, 2012, 层状介质中有限震源引起的地面运动的计算——对点源情形的拓展: 地震学报, 34(4), 425-438. (Link) (PDF) Citation in English: Di, H., and L. Xu, 2012, Calculation of the ground motion generated by a finite source in stratified media: An extension of the point-source case: Acta Seismologica Sinica (in Chinese), 34(4), 425-438.

  40. 邸海滨, 郭玉倩, 刘喜武, 2011, 基于稀疏约束贝叶斯估计的相对波阻抗反演: 石油物探, 50(2), 124-128. (Link) (PDF)  Citation in English: Di, H., Y. Guo, and X.W. Liu, 2011, The inversion algorithm of seismic relative acoustic impedance based on Cauchy sparseness constraint Bayesian estimation: Geophysical Prospecting for Petroleum (in Chinese), 50(2), 124-128. 

  41. 许力生, 邸海滨, 冯万鹏, 李春来, 2010, 2010年青海玉树Ms 7.1地震近断层地面运动估计: 地球物理学报, 53(6), 1366-1373. (Link) (PDF) Citation in English: Xu, L., H. Di, W. Feng, et al, 2010, Estimation of the fault-near ground motion of the 2010 Yushu, Qinghai, Ms7.1 earthquake: Chinese J. Geophys. (in Chinese), 53(6),1366-1373. 

Journal Papers
Conference (Expanded) Abstracts

  1. Di, H., and A. Abubakar, 2023, Stochastic windfarm soil property estimation via deep CNNs: 4th EAGE Global Energy Transition Conference and Exhibition, 1-5. (Link) (PDF)

  2. Dhar Gupta, K., H. Di, and A. Abubakar, 2023, Automatic estimation of reservoir properties using 3D machine learning workflow independent of well alignment in the presence of geological complexes: Abu Dhabi International Petroleum Exhibition & Conference, SPE-216867-MS. (Link) (PDF)

  3. Di, H., and A. Abubakar, 2023, Structure-constrained windfarm soil property estimation via deep neural networks: 84th EAGE Annual Conference and Exhibition, 1-5. (Link) (PDF)

  4. Di, H., and A. Abubakar, 2023, Estimating elastic properties from angle-stack seismic data via deep neural networks: 84th EAGE Annual Conference and Exhibition, 1-5. (Link) (PDF)

  5. Shao, T., W. Hu, S. Phan, H. Di, and A. Abubakar, 2023, Time-lapse seismic data deblending with deep learning: 84th EAGE Annual Conference and Exhibition, 1-5. (Link) (PDF)

  6. Zhao, T., H. Di, and A. Abubakar, 2023, Cascaded deep learning for offshore wind farm 2D seismic horizon interpretation: 84th EAGE Annual Conference and Exhibition, 1-5. (Link) (PDF)

  7. Di, H., and A. Abubakar, 2023, An integrated workflow for windfarm site characterization by deep learning: 3rd EAGE Digitalization Conference and Exhibition, 1-6. (Link) (PDF)

  8. Abubakar, A., H. Di, S. Manikani, and T. Zhao, 2023, How deep learning can accelerate offshore wind farm site characterization: AAPG Explorer. (Link)

  9. Di, H., and A. Abubakar, 2022, Accelerating geotechnical soil characterization in offshore windfarm sites via semi-supervised learning: 3rd EAGE Global Energy Transition Conference & Exhibition. (Link) (PDF)

  10. Di, H., and A. Abubakar, 2022, Self-Supervised learning for automated seismic wavelet extraction: Abu Dhabi International Petroleum Exhibition & Conference, SPE-211823-MS. (Link) (PDF)

  11. Di, H., and A. Abubakar, 2022, Semi-supervised learning for geotechnical soil property estimation in offshore windfarm sites: Abu Dhabi International Petroleum Exhibition & Conference, SPE-211836-MS. (Link) (PDF)

  12. Di., H., V. Simoes, Z. Li, C. Li, A. Kaul, and A. Abubakar, 2022, An integrated workflow for deep learning-accelerated seismic modeling of the Groningen gas field, the Netherlands: 92nd SEG Technical Program Expanded Abstracts, 1805-1809. (Link) (PDF)

  13. Zhao, T., H. Di, and A. Abubakar, 2022, Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction: 92nd SEG Technical Program Expanded Abstracts, 1800-1804. (Link) (PDF)

  14. Di, H., L. Truelove, and A. Abubakar, 2022, Automated active learning for seismic facies classification: 92nd SEG Technical Program Expanded Abstracts, 1694-1698. (Link) (PDF)

  15. Di, H., A. Abubakar, 2022, Unsupervised learning for automated seismic-well tie: 83rd EAGE Annual Conference and Exhibition. (Link) (PDF)

  16. K. Dhar Gupta, H. Di, and A. Abubakar, 2022, Improving automatic estimation of rock properties in the presence of geological complexes using machine learning: 83rd EAGE Annual Conference and Exhibition. (Link) (PDF)

  17. Di, H., C. K. Kloucha, C. Li, A. Abubakar, Z. Li, H. B. Jeddou, and H. Mustapha, Fault-guided seismic stratigraphy interpretation via semi-supervised learning: Abu Dhabi International Petroleum Exhibition & Conference, SPE-207218-MS. (Link) (PDF)

  18. Di, H., Z. Li, and A. Abubakar, 2021, Using relative geologic time to constrain seismic facies classification using neural networks: 91st SEG Technical Program Expanded Abstracts, 991-995. (Link) (PDF)

  19. Abubakar, A., M. J. Brædstrup, H. Di, A. T. Diaz, S. Freeman, S. Hviid, K. H. Karkov, S. Kriplani, S. Manikani, G. Salun, and T. Zhao, Deep learning applications for wind farms site characterization and monitoring: 91st SEG Technical Program Expanded Abstracts, 3009-3013. (Link) (PDF)

  20. Zhao, T., B. Sankaranarayanan, H. Di, and A, Abubakar, Using backward models to validate the goodness-of-fit in machine learning predictions: 91st SEG Technical Program Expanded Abstracts, 1435-1439. (Link) (PDF)

  21. Di, H., and A. Abubakar, 2021, Using semi-supervised convolutional neural networks for porosity modeling over a fluvio-deltaic Triassic gas field: SPE Annual Technical Conference and Exhibition, SPE-205841-MS. (Link) (PDF)

  22. Cai, A., H. Di, Z. Li, H. Maniar, and A. Abubakar, 2020, Wasserstein cycle-consistent generative adversarial network for improved seismic impedance inversion: Example on 3D SEAM model: 90th SEG Technical Program Expanded Abstracts, 1274-1278. (Link) (PDF)

  23. Di, H., X. Chen, H. Maniar, and A. Abubakar, 2020, Semi-supervised seismic and well log integration for reservoir property estimation: 90th SEG Technical Program Expanded Abstracts, 2166-2170. (Link) (PDF)

  24. Guo, R., H. Maniar, H. Di, N. Moldoveanu, A. Abubakar, and M. Li, 2020, Ground roll attenuation with an unsupervised deep learning approach: 90th SEG Technical Program Expanded Abstracts, 3164-3168. (Link) (PDF)

  25. Bhattacharya, S., and H. Di, 2020, The classification and interpretation of the polyphase fault network on the North Slope, Alaska using deep learning: 90th SEG Technical Program Expanded Abstracts, 3847-3851. (Link) (PDF)

  26. Di, H., X. Chen, H. Maniar, and A. Abubakar, 2020, A semi-supervised learning framework for seismic acoustic impedance estimation: EAGE/AAPG Digital Subsurface for Asia Pacific Conference, 1-3 (Link) (PDF)

  27. Di, H., C. Li, S. Smith, and A. Abubakar, 2019, Machine learning-assisted seismic interpretation with geologic constraints: 89th SEG Technical Program Expanded Abstracts, 5360-5364. (Link) (PDF)

  28. Di, H., Z. Li, H. Maniar, and A. Abubakar, 2019, Seismic stratigraphy interpretation via deep convolutional neural networks: 89th SEG Technical Program Expanded Abstracts, 2358-2362. (Link) (PDF)

  29. Li, Z., H. Di, H. Maniar, and A. Abubakar, 2019, Semi-supervised deep machine learning assisted seismic image segmentation and stratigraphic sequence interpretation: 81st EAGE Conference & Exhibition, Th_P04_04. (Link) (PDF)

  30. Di, H., Z. Wang, and G, AlRegib, 2018, Real-time seismic image interpretation via deconvolutional neural network: 88th SEG Technical Program Extended Abstracts, 2051-2055. (Link) (PDF)

  31. Di, H., Z. Wang, and G, AlRegib, 2018, Why using CNN for seismic interpretation? An investigation: 88th SEG Technical Program Extended Abstracts, 2216-2220. (Link) (PDF)

  32. Di, H., Shafiq, M., and G. AlRegib, 2018, Patch-level MLP classification for improved fault detection: 88th SEG Technical Program Extended Abstracts, 2211-2215. (Link) (PDF)

  33. Shafiq, M., M. Prabhushankar, H. Di, and G. AlRegib, 2018, Towards understanding common features between natural and seismic images: 88th SEG Technical Program Extended Abstracts, 2076-2080. (Link) (PDF)

  34. Di, H., Z. Wang, and G. AlRegib, 2018, Seismic fault detection from post-stack amplitude by convolutional neural networks: 80th EAGE Conference and Exhibition, Tu-D-11. (Link) (PDF) (Slides)

  35. Di, H., Z. Wang, and G. AlRegib, 2018, Deep convolutional neural networks for seismic salt-body delineation: AAPG Annual Convention and Exhibition, Search and Discovery Article #90323. (Link)

  36. Shafiq, M., Z. Long, H. Di, G. AlRegib, and M. Deriche, 2018, Fault detection using attention models based on visual saliency: ICASSP 2018. (Link)

  37. Di, H., and G. AlRegib, 2017, A new method for dip estimation based on seismic waveform curvature/flexure analysis: 87th SEG Technical Program Extended Abstracts, 2260-2264. (Link) (PDF) (Slides)

  38. Di, H., M. Alfarraj, and G. AlRegib, 2017, 3D curvature analysis of seismic waveform and its interpretational implications: 87th SEG Technical Program Extended Abstracts, 2255-2259. (Link) (PDF) (Slides)

  39. Di, H., M. A. Shafiq, and G. AlRegib, 2017, Seismic fault detection based on multi-attribute support vector machine analysis: 87th SEG Technical Program Extended Abstracts, 2039-2044. (Link) (PDF) (Slides)

  40. Alfarraj, M., H. Di, and G. AlRegib, 2017, Multiscale fusion for seismic geometric attribute enhancement: 87th SEG Technical Program Extended Abstracts, 2310-2314. (Link) (PDF) (Slides)

  41. Huang, Y., and H. Di, 2017, Dip interpolation for improved multi-trace seismic attribute analysis: 87th SEG Technical Program Extended Abstracts, 2091-2095. (Link) (PDF)

  42. Shafiq, M. A., Y. Alaudah, H. Di, and G. AlRegib, 2017, Salt dome detection within migrated seismic volumes using phase congruency: 87th SEG Technical Program Extended Abstracts, 2360-2365. (Link) (PDF) (Poster)

  43. Di, H., M. Shafiq, and G. AlRegib, 2017, Multi-attribute k-means cluster analysis for salt boundary detection: 79th EAGE Conference and Exhibition, Tu-B4-09. (Link) (PDF)

  44. Di, H., and G. AlRegib, 2017, Seismic multi-attribute classification for salt boundary detection: A comparison: 79th EAGE Conference and Exhibition, Tu-B4-13. (Link) (PDF)

  45. Alaudah, Y., H. Di, and G. AlRegib, Weakly supervised seismic structure labelling via orthogonal non-negative matrix factorization: 79th EAGE Conference and Exhibition, Tu-B4-15. (Link) (PDF)

  46. Alfarrj, M., H. Di, and G. AlRegib, 2017, Multiscale fusion for improved instantaneous attribute analysis: 79th EAGE Conference and Exhibition, Th-A4-13. (Link) (PDF)

  47. Shafiq, M., H. Di, and G. AlRegib, 2017, A texture-based approach for automated detection of listric faults: 79th EAGE Conference and Exhibition, Th-P4-02. (Link) (PDF)

  48. Di, H., and D. Gao, 2016, Improved seismic texture analysis based on non-linear gray-level transformation: 86th SEG Technical Program Expanded Abstracts, 2093-2097. (Link) (PDF)

  49. Di, H., and D. Gao, 2015, Reflection geometry-based strain analysis from 3D seismic data: 85th SEG Technical Program Expanded Abstracts, 1922-1926. (Link) (PDF)

  50. Di, H., and D. Gao, 2014, A new analytical method for azimuthal curvature analysis from 3D seismic data: 84th SEG Technical Program Expanded Abstracts, 1634-1638. (Link) (PDF)

  51. Di, H., and D. Gao, 2014, Predicting fracture orientations with volumetric curvature gradient analysis: Case study from Teapot Dome in Wyoming: AAPG Annual Convention and Exhibition, Search and Discovery Article #41331. (Link) (PDF)

  52. Di, H., and D. Gao, 2013, Gray-level transformation and Canny edge detection for 3D seismic discontinuity enhancement: 83rd SEG Technical Program Expanded Abstracts, 1504-1508. (Link) (PDF)

  1. Bhattacharya, S., and H. Di, 2022, Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning, Elsevier (Edited book). (Link)

  2. Di, H., and D. Gao, 2017, Seismic attribute-aided fault detection in petroleum industry: A review, in D. Martin (eds), Fault detection: Methods, Applications and Technology, 53-80. (Link) (PDF)

Books & Chapters
Dissertation & Thesis

May 2016

Seismic geometric attribute analysis for fracture characterization: New Methodologies and applications: Ph.D. Dissertation, West Virginia University. (Link) (PDF)

 

July 2011

分层均匀介质中有限震源引起的地面运动计算: 硕士论文, 中国地震局地球物理研究所 (Link) (PDF) Citation in English: Calculation of the ground motion caused by a finite source in stratified media: M.S. Thesis (in Chinese), Institute of Geophysics, China Earthquake Administration.

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