Skip to main content

Advertisement

SpringerLink
Log in

Rock thin sections identification under harsh conditions across regions based on online transfer method

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

Automatic identification of rock thin sections provides valuable geological information for oil and gas exploration. However, the application of rock thin sections identification in new blocks works poorly. The reason is that the geological data of target blocks are extremely scarce in the early stage of exploration and development. Therefore, using an improved MaSE-ResNetXt network, data enhancement, feature extractors, online transfer learning and other strategies, a stable, efficient and continuously growing end-to-end identification system based on online transfer learning was built.

In case one, the use of the basic MaSE-ResNetXt network on regional rock properties and model categories was compared with previous studies and experiments, and performance improvements of 5% and 14.3% were achieved respectively, which verified the high efficiency of the basic MaSE-ResNetXt network for feature extraction in a large-scale dataset. In case two, sensitivity analysis was conducted for similar categories that are easily confused, during which parameters of the network and optimizer were compared. The sensitivity problems of oolitic structure feature maps were discussed and optimized. After that, a weighted F1-score of 0.95 was reached, proving the prediction stability of cross-regional transfer. In case three, real-time online transfer learning for the cold boot was conducted with scarce geological data. Compared with the previous studies, the training speed was improved by 15 times, and the accuracy was improved by 20%.

An improved method based on MaSE-ResNeXt with transfer learning using a neural network feature map for rock characteristics analysis was proposed. By proposing inheritance and real-time online transfer learning solutions, the problems of automatic rock thin sections identification in a new area were solved, and the geological data of multiple blocks were fully used. By doing so, this study bridged the gap that experiences from the former regions could not be inherited effectively for cross-regional rock thin sections identification, so that empowered the development of new blocks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data availability

https://www.scidb.cn/en/detail?dataSetId=732953783604084736&language=null&dataSetType=journal

There are the interpretations in English in the Github repository.

References

  1. Budennyy, S., Pachezhertsev, A., Bukharev, A., Erofeev, A., Mitrushkin, D., Belozerov, B., 2017. Image Processing and Machine Learning Approaches for Petrographic Thin Section Analysis. OnePetro. https://doi.org/10.2118/187885-MS

  2. Caja, M.Á., Peña, A.C., Campos, J.R., García Diego, L., Tritlla, J., Bover-Arnal, T., Martín-Martín, J.D., 2019. Image Processing and Machine Learning Applied to Lithology Identification, Classification and Quantification of Thin Section Cutting Samples. OnePetro. https://doi.org/10.2118/196117-MS

  3. Chai, H., 2020: A carbonate micrograph dataset of Feixianguan formation in northwestern margin of upper Y angtze. Science data Bank. https://doi.org/10.11922/sciencedb.j00001.00021

  4. Chai, H., Xing, F., Gu, Q., Chen, X., Zhou, S.: A carbonate micrograph dataset of Feixianguan formation in northwestern margin of upper Y angtze. China Sci. Data. 5, 131–140 (2020)

    Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778.

  6. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527

    Article  Google Scholar 

  7. Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132–7141. https://doi.org/10.48550/arXiv.1709.01507

  8. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q., 2017: Densely connected convolutional networks, in: 30th Ieee conference on computer vision and pattern recognition (Cvpr 2017). Pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243

  9. Ioffe, S., Szegedy, C., 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning. PMLR, pp. 448–456.

  10. Lai, W., 2020a: A photomicrograph dataset of rocks for petrology teaching at Nanjing University. Science data Bank. https://doi.org/10.11922/sciencedb.j00001.00097

  11. Lai, W., 2020b: Photomicrograph dataset of cretaceous siliciclastic rocks from Central-Northern Lhasa terrane, Tibet. Science data Bank. https://doi.org/10.11922/sciencedb.j00001.00021

  12. Lai, W., Jiang, J., Qiu, J.: A photomicrograph dataset of rocks for petrology teaching at Nanjing University. China Sci. Data. 5, 26–38 (2020)

    Google Scholar 

  13. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE. 86, 2278–2324 (1998)

    Article  Google Scholar 

  14. Liang, X., Zhou, F., Liang, T., Su, H., Yuan, S., Li, Y.: Impacts of pore structure and wettability on distribution of residual fossil hydrogen energy after imbibition. Int. J. Hydrog. Energy. 45, 14779–14789 (2020a). https://doi.org/10.1016/j.ijhydene.2020.03.208

    Article  Google Scholar 

  15. Liang, X., Zhou, F., Liang, T., Wang, C., Li, Y.: Experimental quantification of formation damage caused by the cross-linked gel in tight gas reservoirs. J. Nat. Gas Sci. Eng. 84, 103608 (2020b). https://doi.org/10.1016/j.jngse.2020.103608

    Article  Google Scholar 

  16. Liang, X., Zhou, F., Liang, T., Wang, C., Wang, J., Yuan, S.: Impacts of low harm fracturing fluid on fossil hydrogen energy production in tight reservoirs. Int. J. Hydrog. Energy. 45, 21195–21204 (2020c). https://doi.org/10.1016/j.ijhydene.2020.06.011

    Article  Google Scholar 

  17. de Lima, R.P., Duarte, D., Nicholson, C., Slatt, R., Marfurt, K.J.: Petrographic microfacies classification with deep convolutional neural networks. Comput. Geosci. 142, 104481 (2020). https://doi.org/10.1016/j.cageo.2020.104481

    Article  Google Scholar 

  18. Ma, H., Han, G., Peng, L., Zhu, L., Shu, J.: Rock thin sections identification based on improved squeeze-and-excitation networks model. Comput. Geosci. 152, 104780 (2021)

    Article  Google Scholar 

  19. Marmo, R., Amodio, S., Tagllaferri, R., Ferreri, V., Longo, G.: Textural identification of carbonate rocks by image processing and neural network: methodology proposal and examples. Comput. Geosci. 31, 649–659 (2005). https://doi.org/10.1016/j.cageo.2004.11.016

    Article  Google Scholar 

  20. Mlynarczuk, M., Gorszczyk, A., Slipek, B.: The application of pattern recognition in the automatic classification of microscopic rock images. Comput. Geosci. 60, 126–133 (2013). https://doi.org/10.1016/j.cageo.2013.07.015

    Article  Google Scholar 

  21. Pattnaik, S., Chen, S., Shao, W., Helba, A., 2020. Automating Microfacies Analysis of Petrographic Images. OnePetro. https://doi.org/10.30632/SPWLA-5074

  22. Polat, Ö., Polat, A., Ekici, T.: Automatic classification of volcanic rocks from thin section images using transfer learning networks. Neural Comput. & Applic. 33, 1–10 (2021)

    Article  Google Scholar 

  23. Purba, S.A., Garcia, A.P., Heidari, Z.: A new hierarchical method for rock classification using well-log-based rock fabric quantification. Petrophysics. 59, 720–734 (2018). https://doi.org/10.30632/PJV59N5-2018a10

    Article  Google Scholar 

  24. Shu, L., McIsaac, K., Osinski, G.R., Francis, R.: Unsupervised feature learning for autonomous rock image classification. Comput. Geosci. 106, 10–17 (2017). https://doi.org/10.1016/j.cageo.2017.05.010

    Article  Google Scholar 

  25. Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. ArXiv Prepr. ArXiv14091556. https://doi.org/10.48550/arXiv.1409.1556

  26. Singh, N., Singh, T.N., Tiwary, A., Sarkar, K.M.: Textural identification of basaltic rock mass using image processing and neural network. Comput. Geosci. 14, 301–310 (2010). https://doi.org/10.1007/s10596-009-9154-x

    Article  Google Scholar 

  27. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions, in: 2015 Ieee conference on computer vision and pattern recognition (Cvpr). Pp. 1–9. https://doi.org/10.1109/cvpr.2015.7298594

  28. Tagliaferri, R., Longo, G., D’Argenio, B., Incoronato, A.: Introduction: neural networks for analysis of complex scientific data: astronomy and geosciences. Neural Netw. 16, 295–295 (2003). https://doi.org/10.1016/s0893-6080(03)00012-1

    Article  Google Scholar 

  29. Zeiler, M.D., Fergus, R., 2014. Visualizing and understanding convolutional networks, in: European Conference on Computer Vision. Springer, pp. 818–833. https://doi.org/10.1007/978-3-319-10590-1_53

Download references

Acknowledgments

The authors would like to express their gratitude to the reviewers of China University of Petroleum - Beijing for their critical comments.

Code availability

Repository: https://github.com/mmmahhhhe/MaSeResNeXt

Author information

Authors and Affiliations

  1. China University of Petroleum – Beijing, 18 Fuxue Road, Changping District, Beijing, 102249, China

    Biao Wang, Guoqing Han, He Ma, Liying Zhu, Xingyuan Liang & Xin Lu

  2. Block D, International Investment Building, Fuchengmen North Street, Xicheng District, Beijing, 100034, China

    Liying Zhu

Authors
  1. Biao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoqing Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liying Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingyuan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoqing Han.

Ethics declarations

Conflicts of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Rock thin sections identification for industrial applications.

Large-scale and multi-scale applicability under harsh conditions.

Improved Squeeze-and-Excitation Networks.

Fundamentals of transfer learning in petrology.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Han, G., Ma, H. et al. Rock thin sections identification under harsh conditions across regions based on online transfer method. Comput Geosci 26, 1425–1438 (2022). https://doi.org/10.1007/s10596-022-10174-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-022-10174-2

Keywords

Search

Navigation