We currently focus on deep learning for geoscience problems including seismic and well-log interpretation, seismic imaging, inversion, seismology and marine data processing and so on.


Han, L., X. Wu*, Z. Hu, J. Li, and H. Fang, 2024, MAMCL: Multi-attributes Masking Contrastive Learning for explainable seismic facies analysis, submitted.

Yang, Z., X. Wu*, X. Pang, H. Sheng, X. Si, G. Wang, L. Yang, and C. Wang, 2024, Completing any borehole images, submitted.

Xue, Z., Y. Wang, X. Wu*, and J. Ma*, 2024, Multi-Geophysical Information Neural Network for Seismic Tomography, Geophysics, in revision.

Wang, G., X. Wu*, and W. Zhang, 2024, cigChannel: a massive-scale 3D seismic data with labeled paleochannels for advancing deep learning in seismic interpretation, ESSD, submitted.

Gao, H., X. Wu*, S. Xiao, M. Hou, H. Gao, G. Wang, and H. Sheng, 2024, cigFacies: a massive-scale benchmark dataset of seismic facies and its application, GRL, submitted.

Li, J., Y. Shi, and X. Wu*, 2024, CIGVis: an open-source Python tool for real-time interactive visualization of multidimensional geophysical data, Geophysics, in revision. [pypi], [CODE], [GIF-1], [GIF-2], [GIF-3].

Xie, D., X. Wu*, Z. Guo, H. Hong, B. Wang, and Y. Rong, 2024, Intelligent traffic monitoring with Distributed Acoustic Sensing (DAS), IEEE Transactions on Intelligent Transportation Systems, submitted. [FIG].

Sheng, H., X. Wu*, X. Si, J. Li, S. Zhang, and X. Duan, 2024, Seismic Foundation Model (SFM): a next generation deep learning model in geophysics, JGR Solid Earth, submitted. [Arxiv]

Gao, H., X. Wu*, and Z. Liao, 2023, Deep learning-based interpretation of fault damage zones in 3D seismic data, JGR Solid Earth, in revision.

Dai, C., X. Si, and X. Wu*, 2023, FlexLogNet: a flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing, Computers & Geosciences, in revision. [FIG].


Li, Y., X. Wu*, Z. Zhu, J. Ding, and Q. Wang, 2024, FaultSeg3D plus: a comprehensive study on evaluating and improving CNN-based seismic fault segmentation, Geophysics, in press. [FIG].

Si, X., X. Wu*, Z. Li*, S. Wang, and J. Zhu, 2024, An all-in-one seismic phase picking, location, and association network for multi-task multi-station earthquake monitoring, Communications Earth & Environment, Vol. 5(22). [FIG], [PDF], [CODE].

Si, X., X. Wu*, H. Sheng, J. Zhu, and Z. Li, 2024, SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction, IEEE TGRS, Vol. 62, pp. 1-13, Art no. 5903713, doi: 10.1109/TGRS.2024.3354456, [Code], [PDF].

Jiang, L., X. Si, and X. Wu*, 2024, Filling borehole image gaps with partial convolution neural network. Geophysics, Vol. 89(2), D289–D98. [PDF]. [FIG].

Wang, F.+, X. Wu*, H. Zeng, X. Janson, and C. Kerans, 2024, Stratal surfaces honoring seismic structures and interpreted geologic time surfaces. Geophysics, Vol. 89(2), N45–N57. [PDF]. [FIG]. (+visiting student)


Wu, X., J. Ma*, S. Xu, Z. Bi, J. Yang, H. Gao, D. Xie, Z. Guo, and J. Zhang, 2023, Sensing prior constraints in deep neural networks for solving geophysical problems, PNAS, Vol. 120(23), e2219573120. [PDF], [SEG China].

Bangs, N.*, J. Morgan, R. Bell, S. Han, R. Arai, S. Kodaira, A. Gase, X. Wu, et al., 2023, Slow slip along the Hikurangi margin linked to fluid-rich sediments trailing subducting seamounts, Nature Geoscience. [PDF].

Jiang, E.+, R. Chen*, X. Wu*, J. Liu, D. Zhu, and W. Liu, 2023, Deep learning based sferics recognition for AMT data processing in the dead band, Geophysics, Vol. 88(5), B233–B250. [PDF]. [FIG]. (+visiting student)

Gao, H., X. Wu*, J. Zhang, X. Sun, and Z. Bi, 2023, ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation. Geoscientific Model Development (GMD), Vol. 16(9), 2495–2513. [PDF]. [FIG].

Yang, J., X. Wu*, Z. Bi, and Z. Geng, 2023, A multi-task learning method for relative geologic time, horizons, and faults with prior information and transformer. IEEE TGRS, vol. 61, pp. 1-20, Art no. 5907720, doi: 10.1109/TGRS.2023.3264593. [PDF]. [FIG].

Xue, Z. and X. Wu*, 2023, Automatic velocity analysis with physics-constrained optimal surface picking, Geophysics, Vol. 88(3), U71–U80. [PDF]. [FIG].

Sheng, H., X. Wu*, X. Sun, and L. Wu, 2023, Deep learning for characterizing CO2 migration in time-lapse seismic images, Fuel, Vol. 336, 126806. [PDF]. [GIF].

Li, J., , X. Wu*, Y. Ye, C. Yang, Z. Hu, X. Sun, and T. Zhao, 2023, Unsupervised contrastive learning for seismic facies characterization, Geophysics, Vol. 88(1), WA81–WA89. [PDF]. [FIG].

Yu, Y., Y. Li, X. Wu, and X. Jia*, 2023, Enhancing one-way wave equation-based migration with deep learning. Geophysics, Vol. 88(1), WA105–WA114. [PDF].

张文, 伍新明*,漆杰,2023,几何地震属性的快速算法实现,地球物理学报,66(8): 3374-3390. [PDF].


Bi, Z., X. Wu*, Z. Li, D. Chang, and X. Yong, 2022, DeepISMNET: 3D Implicit structural modeling with convolutional neural network. Geoscientific Model Development (GMD), Vol. 15(17), 6841–6861. [PDF]. [FIG].

毕钲发, 刘杨, 伍新明*, 吴丽丽, 2022, 基于声呐图像的海底线缆智能实时探测 (Deep learning for realtime detection of submarine cables in sonar images), 地球物理学报, in press。 [FIG].

王腾飞,程玖兵,朱峰,伍新明, 徐蔚亚,耿建华,2023, 超深层目标随钻测井引导的动态地震成像,地球物理学报,2023, 66(1): 54-64. doi: 10.6038/cjg2022Q0390。

Li, Y., X. Jia*, X. Wu, and Z. Geng, 2022, Deep learning for enhancing multi-source reverse time migration, IEEE TGRS, vol. 60, pp. 1-13, 2022, Art no. 4512313, doi: 10.1109/TGRS.2022.3206283. [PDF].

Wu, X., Y. Li, and P. Sawasdee, 2022, Toward accurate seismic flattening: methods and applications. Geophysics, Vol. 87(5), IM177–IM188. [PDF]. [GIF].

伍新明*, 杨家润, 朱振宇, 丁继才, 王清振, 2022, 计算地震构造解释与建模的实现讨论. 石油物探,Vol. 87(3), 392-407. [PDF] (专家论坛文章, 第三届江苏省科技期刊百篇优秀论文)
Wu, X., J. Yang, Z. Zhu, J. Ding, and Q. Wang, 2022, Discussions on computational seismic structural interpretation and modeling. Geophysical Prospecting for Petroleum, Vol. 87(3), 392-407.

Zhang, Q.+, W. Zhang*, X. Wu*, J. Zhang, W. Kuang, and X. Si, 2022, Deep Learning for efficient microseismic location using source migration-based imaging, JGR, Solid Earth, Vol. 127(3), 1-19. [PDF], [FIG]. (+visiting student)

Wang, F.+, X. Wu*, and H. Wang*, 2022, Seismic horizon identification using semi-supervised learning with virtual adversarial training. IEEE TGRS, vol. 60, pp. 1-11, Art no. 4508611, doi: 10.1109/TGRS.2022.3154439. (+visiting student)

Yan, S., X. Sun, X. Wu*, S. Zhang, and H. Si, 2022, Building subsurface models with horizon-guided interpolation and deep learning: applied to the Volve field dataset. Geophysics, Vol. 87(4), B233–B245. [PDF]. [FIG].

Sheng, H., X. Wu*, and B. Zhang, 2022, Wavelet estimation and non-stretching NMO correction, Geophysics. Vol. 87(3), V193–V203. [PDF]. [FIG].

Zhang S., H. Si, X. Wu*, and S. Yan, 2022, A comparison of deep learning methods for seismic impedance inversion. Petroleum Science, Vol. 19(3), 1019-1030. [PDF]. [FIG].

Geng, Z., Z. Hu, X. Wu*, and S. Fomel, 2022, Semi-supervised salt segmentation using mean teacher. Interpretation, Vol. 10(3), SE21-SE29. [PDF].

Bi, Z., X. Wu*, Y. Li, S. Yan, S. Zhang, and H. Si, 2022, Geologic time based interpolation of borehole data for building high-resolution models: methods and applications. Geophysics, Vol. 87(3), IM67–IM80. [PDF].

Geng, Z., Z. Zhao, Y. Shi, X. Wu, S. Fomel, and M. Sen, 2022, Deep learning for velocity model building with common-image gathers. GJI, 228, 1054–1070. [PDF].


49. Wu, X., Y. Shi, and S. Fomel, 2022, Using synthetic data sets to train a neural network for three-dimensional seismic fault segmentation. US Patent 11403495. [PDF].

48. Bi, Z., X. Wu*, Z. Geng, and H. Li, 2021, Deep relative geologic time: a deep learning method for simultaneously interpreting 3D seismic horizons and faults, JGR, Solid Earth, Vol. 126 (9), 1-24. [PDF].

47. Wu, X.*, S. Yan, Z. Bi, S. Zhang, and H. Si, 2021, Deep learning for multi-dimensional seismic impedance inversion. Geophysics, Vol. 86(5), R735–R745. [PDF].

46. Gao, H., X. Wu*, and G. Liu, 2021, ChannelSeg3D: channel simulation and deep learning for channel interpretation in 3D seismic images. Geophysics, Vol. 86(4), IM73–IM83. [PDF].

45. Liang, L., S. Deng, L. Gueguen, M. Wei, X. Wu, J. Qin, 2021, Convolutional neural network with median layers for denoising salt-and-pepper contaminations, Neurocomputing. 442, 26-35. [PDF].

44. Li, J.,, X. Wu*, and Z. Hu, 2021, Deep learning for simultaneous seismic super-resolution and denoising. IEEE TGRS, Vol. 60, pp. 1-11, Art no. 5901611, doi: 10.1109/TGRS.2021.3057857. [PDF].

43. Yan, S. and X. Wu*, 2021, Seismic horizon extraction with dynamic programming. Geophysics, Vol. 86(2), A15-W19. [PDF].

42. Shi, Y., X. Wu*, and S. Fomel, 2021, Interactively tracking seismic geobodies with a deep learning flood-filling network. Geophysics, Vol. 86(1), A1–A5. [PDF].

41. Bi, Z. and X. Wu*, 2021, Improving fault surface construction with inversion-based methods. Geophysics, Vol. 86(1), IM1–IM14. [PDF].


40. Niu, L., J. Geng, X. Wu, L. Zhao, and H. Zhang, 2020, Data-driven method for an improved linearized AVO inversion. JGE, in press. [PDF].

39. Wu, X., S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. JGR, Solid Earth, Vol. 125(9), 1-23, e2020JB019685. [PDF]. doi: 10.1029/2020JB019685

38. Merzlikin, D., S. Fomel, and X. Wu, 2020, Least-squares diffraction imaging using shaping regularization by anisotropic smoothing. Geophysics, Vol. 85(5), P. S313–S325. [PDF].

37. Zhi, Z., A. Sun, and X. Wu, 2020, Inversion of Time-lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning Based Approach for Estimating Dynamic Reservoir Property Changes. JGR Solid Earth, Vol. 125 (3), [PDF].

36. Li, F., H. Zhou, and Z. Wang, X. Wu, 2020, ADDCNN: An Attention-Based Deep Dilated Convolutional Neural Network for Seismic Facies Analysis With Interpretable Spatial-Spectral Maps. IEEE TGRS, vol. 59, no. 2, pp. 1733-1744. [PDF].

35. Wu, X., Z. Geng, Y. Shi, N. Pham, S. Fomel, and G. Caumon, 2020, Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, Vol. 85(4), WA27-WA39. [PDF]. (Published in the special section: Machine Learning and Data Analytics for Geoscience Applications). (One of the most downloaded Geophysics paper, accessed 3rd Dec.,2020).

34. Geng, Z., X. Wu*, Y. Shi, and S. Fomel, 2020, Deep learning for relative geologic time and seismic horizons. Geophysics, Vol. 85(4), WA87–WA100. [PDF]. (Published in the special section: Machine Learning and Data Analytics for Geoscience Applications).

33. Shi, Y., X. Wu*, and S. Fomel, 2020, Waveform embedding: automatic horizon picking with unsupervised deep learning. Geophysics, Vol. 85(4), WA67–WA76. [PDF]. (Published in the special section: Machine Learning and Data Analytics for Geoscience Applications).

32. Pham, N., X. Wu*, and E. Naeini, 2020, Missing well log prediction using convolutional long short-term memory network. Geophysics, Vol. 85(4), WA159–WA171. [PDF]. (Published in the special section: Machine Learning and Data Analytics for Geoscience Applications).

31. Geng, Z., X. Wu, S. Fomel, Y. Chen, 2020, Relative-time seislet Transform. Geophysics, Vol. 85(2), V223-V232. [PDF]


30. Wu, X., L. Liang, Y. Shi, Z. Geng and S. Fomel, 2019, Multi-task learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single CNN. GJI, Vol. 210(3), 2097-2109. [PDF]

29. D., H., T. Zhao, V. Jayaram, X. Wu et al., 2019, Introduction to special section: Machine learning in seismic data analysis. Interpretation, 7(3), pp. SEi–SEii.

28. Wu, X., H. Zeng, H. Di et al., 2019, Introduction to special section: seismic geometric attributes. Interpretation, Vol. 7(2).

27. Wu, X., Y. Shi, S. Fomel, L. Liang, Q. Zhang and A. Yusifov, 2019, FaultNet3D: predicting fault probabilities, strikes and dips with a single convolutional neural network. IEEE TGRS, Vol. 57(11), 9138 - 9155. [PDF]

26. Shi, Y., X. Wu* and S. Fomel, 2019, SaltSeg: Automatic 3D salt body segmentation using a deep convolutional neural network. Interpretation, SE113-SE122. [PDF]

25. Zhong, Z., T. Carr, X. Wu* and G. Wang, 2019, Application of convolutional neural network in permeability prediction: A case study in Jacksonburg-Stringtown Oil Field, West Virginia, USA. Geophysics, Vol. 84(6), B363–B373. [PDF]

24. Wu, X., L. Liang, Y. Shi and S. Fomel, 2019, FaultSeg3D: using synthetic datasets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, Vol. 84(3), IM35-IM45. [PDF] [CODE]. (Highly cited paper).

23. Bader, S., X. Wu* and S. Fomel, 2019, Missing log data interpolation and semiautomatic seismic well ties using data matching techniques. Interpretation, Vol. 7(2), T347-T361. [PDF] Best Student Poster Paper at the 2017 SEG Annual Convention

22. Wu, X. and Z. Guo, 2019, Detecting faults and channels while enhancing seismic structural and stratigraphic features. Interpretation, Vol. 7(1), T155–T166. [PDF] [CODE]


21. Wu, X. and S. Fomel, 2018, Automatic fault interpretation with optimal surface voting. Geophysics, Vol. 83(5), O67-O82. [PDF] [CODE] (one of xinming's favorite work.)
The corresponding software package has been widely used in industry.

20. Wu, X. and S. Fomel, 2018, Least-squares horizons with local slopes and multi-grid correlations, Geophysics, Vol. 83(4), IM29–IM40. [PDF] [CODE]
Best Paper, Honorable Mention, 2018.

19.Wu, X., S. Fomel, and M. Hudec, 2018, Fast salt boundary interpretation with optimal path picking. Geophysics, Vol. 83(3), O45–O53. [PDF] [CODE]

18. Wu, X., Y. Shi, S. Fomel, and F. Li, 2018, Incremental correlation of multiple well logs following geologically optimal neighbors. Interpretation, Vol. 6(3), T713–T722. [PDF]

17. Wang, C., Z. Zhu, H. Gu, X. Wu, and S. Liu, 2018, Hankel low-rank approximation for seismic noise attenuation. IEEE Transactions on Geoscience and Remote Sensing. Vol. 57(1), 561–573. [PDF]

16. Xue, Z., X., Wu and S. Fomel, 2018, Predictive painting across faults. Interpretation, Vol. 6(2),T449–T455. [PDF]

15. Li, G., Z., Zhu, Z., X., Wu and P. Hou, 2018, On joint optimization of sensing matrix and sparsifying dictionary for robust compressed sensing systems. Digital Signal Processing, Vol. 73, 62–71. [PDF]


14. Wu, X., 2017, Structure-, stratigraphy-, and fault-guided regularization in geophysical inversion. Geophysical Journal International, Vol. 210(1), 184-195. [PDF]

13. Tong, B., I., Tsvankin, and X., Wu, 2017, Waveform inversion for attenuation estimation in anisotropic media. Geophysics, Vol. 82(4), WA83-WA93. [PDF]

12. Wu, X. and Z. Zhu, 2017, Methods to enhance seismic faults and construct fault surfaces. Computers & Geosciences, Vol. 107, 37-48. [PDF]

11. Wu, X. and X., Janson, 2017, Directional structure tensors in estimating seismic structural and stratigraphic orientations. Geophysical Journal International, Vol. 210(1), 534-548. [PDF]

10. Wu, X., 2017, Building 3D subsurface models conforming to seismic structural and stratigraphic features. Geophysics, Vol.82(3), IM21-IM30. [PDF]

09. Wu, X. and G. Caumon, 2017, Simultaneous multiple well-seismic ties with flattened synthetic and real seismograms. Geophysics, Vol. 82(1), IM13-IM20. [PDF]

08. Wu, X., 2017, Directional structure-tensor based coherence to detect seismic channels and faults. Geophysics, Vol. 82(2), A13-A17. [PDF]
Recognized as “Geophysics Bright Spots” by The Leading Edge


07. Wu, X. and D. Hale, 2016, 3D seismic image processing for faults. Geophysics, 81(2), IM1–IM11. [PDF] [CODE]
Technical talk:
2016 Best Paper Award in Geophysics

06. Wu, X., 2016, Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. Geophysics, 81(6), IM119-IM126. [PDF]

05. Wu, X. and D. Hale, 2016, Automatically interpreting all faults, unconformities, and horizons from 3D seismic images. Interpretation, 4(2), T227-T237. [PDF]

04. Wu, X., S. Luo, and D. Hale, 2016, Moving faults while unfaulting 3D seismic images. Geophysics, 81(2), IM25-IM33. [PDF]
Technical talk:
Recognized as “Geophysics Bright Spots” by The Leading Edge


03. Wu, X. and D. Hale, 2015, 3D seismic image processing for unconformities. Geophysics, 80 (2), IM35-IM44. [PDF]
Technical talk:
Recognized as “Geophysics Bright Spots” by The Leading Edge

02. Wu, X. and D. Hale, 2015, Horizon volumes with interpreted constraints. Geophysics, 80 (2), IM21-IM33. [PDF]
Technical talk:
Recognized as “Geophysics Bright Spots” by The Leading Edge

01. Wu, X. and G. Zhong, 2012, Generating a relative geologic time volume by improved 3D graph-cut-based phase unwrapping method with horizon and unconformity constraints. Geophysics, 77 (4), O21-O34. [PDF]


05. 伍新明, 毕钲发, 2023, 基于多源异构数据的地质构造建模方法, CN115587537A.

04. 张思博, 司宏杰, 李壮壮, 伍新明, 2023, 一种地震数据构造仿真方法及装置,CN115826047A.

03. Wu, X., Y. Shi, and S. Fomel, 2022, Using synthetic data sets to train a neural network for three-dimensional seismic fault segmentation. US Patent 11403495. [PDF].

02. 张思博, 司宏杰, 伍新明, 毕钲发, 闫上升, 2022, 一种地震反演方法、装置及系统, CN114063161A.

01. 牛丽萍, 伍新明, 赵峦啸, 麻纪强, 2020, 一种改进的高精度AVO弹性参数快速反演方法, CN109490964B.