Abstract
In this paper, we attempt to provide a new benchmark for image seismic interpretation tasks in a public seismic dataset (Netherlands F3 Block). For this, techniques such as data augmentation together with five different deep network architectures were used, as well as the application of focal loss function. Our experiments achieved an improvement in all evaluation metrics cited at the current benchmark. For instance, we managed to improve in \(3.7\%\) the pixel accuracy metric and \(5.4\%\) on mean class accuracy for a modified U-Net that uses dilated convolution layers in its bottleneck. In addition to this, the confusion matrices of each model are shown for a better inspection in the classes (sedimentary facies) where the greatest amount of misclassification occurred. The training process of almost all networks took less than one hour to converge. Finally, we applied Conditional Random Fields (CRF) as post-processing in order to obtained smother results. The inferences performed with the best topology, in an inline or section of the test set, is closer to achieving an interpretation at a human level.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A Machine Learning Benchmark for Facies Classification: https://github.com/yalaudah/facies_classification_benchmark.
References
Carvana Image Masking Challenge (2017). https://www.kaggle.com/c/carvana-image-masking-challenge
TGS Salt Identification Challenge (2018). https://www.kaggle.com/c/tgs-salt-identification-challenge
Alaudah, Y., Michałowicz, P., Alfarraj, M., AlRegib, G.: A machine-learning benchmark for facies classification. Interpretation 7(3), SE175–SE187 (2019). https://doi.org/10.1190/INT-2018-0249.1
Babakhin, Y., Sanakoyeu, A., Kitamura, H.: Semi-supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) DAGM GCPR 2019. LNCS, vol. 11824, pp. 218–231. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33676-9_15
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chollet, F.: Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. https://doi.org/10.1109/CVPR.2017.195
Civitarese, D., Szwarcman, D., Brazil, E.V., Zadrozny, B.: Semantic segmentation of seismic images. arXiv preprint arXiv:1905.04307 (2019)
Doi, K., Iwasaki, A.: The effect of focal loss in semantic segmentation of high resolution aerial image. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 6919–6922. IEEE (2018)
Dramsch, J.S., Lüthje, M.: Deep-learning seismic facies on state-of-the-art CNN architectures, pp. 2036–2040. Society of Exploration Geophysicists (2018). https://doi.org/10.1190/segam2018-2996783.1. https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, L., Dong, X., Clee, T.E.: A scalable deep learning platform for identifying geologic features from seismic attributes. Lead. Edge 36(3), 249–256 (2017). https://doi.org/10.1190/tle36030249.1
ul Islam, M.S.: Using deep learning based methods to classify salt bodies in seismic images. J. Appl. Geophys. 178, 104054 (2020). https://doi.org/10.1016/j.jappgeo.2020.104054. http://www.sciencedirect.com/science/article/pii/S0926985119307803
Kim, Y., Hardisty, R., Torres, E., Marfurt, K.J.: Seismic facies classification using random forest algorithm. In: SEG Technical Program Expanded Abstracts 2018, pp. 2161–2165. Society of Exploration Geophysicists (2018)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 109–117. Curran Associates, Inc. (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Li, W.: Classifying geological structure elements from seismic images using deep learning, pp. 4643–4648. Society of Exploration Geophysicists (2018). https://doi.org/10.1190/segam2018-2998036.1
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. https://doi.org/10.1109/CVPR.2015.7298965
lyakaap: Kaggle carvana - 3rd place solution (2017). https://github.com/lyakaap/Kaggle-Carvana-3rd-place-solution/
Milosavljević, A.: Identification of salt deposits on seismic images using deep learning method for semantic segmentation. ISPRS Int. J. Geo Inf. 9(1), 24 (2020), https://doi.org/10.3390/ijgi9010024
Piao, S., Liu, J.: Accuracy improvement of UNet based on dilated convolution. J. Phys. Conf. Ser. 1345, 052066 (2019). https://doi.org/10.1088/1742-6596/1345/5/052066
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Salvaris, M., et al.: DeepSeismic: a deep learning library for seismic interpretation, vol. 2020, no. 1, pp. 1–5 (2020). https://doi.org/10.3997/2214-4609.202032086
Silva, R.M., Baroni, L., Ferreira, R.S., Civitarese, D., Szwarcman, D., Brazil, E.V.: Netherlands dataset: a new public dataset for machine learning in seismic interpretation. arXiv preprint arXiv:1904.00770 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Waldeland, A., Solberg, A.: Salt classification using deep learning. In: 79th EAGE Conference and Exhibition 2017, vol. 2017, pp. 1–5. European Association of Geoscientists & Engineers (2017). https://doi.org/10.3997/2214-4609.201700918
Xiong, W., et al.: Seismic fault detection with convolutional neural network. Geophysics 83(5), O97–O103 (2018). https://doi.org/10.1190/geo2017-0666.1
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Zhao, T.: Seismic facies classification using different deep convolutional neural networks, pp. 2046–2050. Society of Exploration Geophysicists (2018). https://doi.org/10.1190/segam2018-2997085.1
Zhao, T., Jayaram, V., Roy, A., Marfurt, K.J.: A comparison of classification techniques for seismic facies recognition. Interpretation 3(4), SAE29-SAE58 (2015). https://doi.org/10.1190/INT-2015-0044.1
Acknowledgments
The authors would like to thank at the Applied Computational Intelligence Laboratory (ICA) and Cenpes/Petrobras, partners for 20Â years in the research and development of artificial intelligence projects for oil and gas sector.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Campos Trinidad, M.J., Arauco Canchumuni, S.W., Cavalcanti Pacheco, M.A. (2021). Towards a Benchmark for Sedimentary Facies Classification: Applied to the Netherlands F3 Block. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., DÃaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-76228-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76227-8
Online ISBN: 978-3-030-76228-5
eBook Packages: Computer ScienceComputer Science (R0)