Salt-body Classification Method Based on U-Net

doi:10.19596/j.cnki.1001-246x.8044

Abstract

Abstract: Convolutional neural network has made great breakthroughs in the field of computer vision. With its powerful image processing ability,we transform classification of underground sedimentary salt-body into image semantics segmentation problem. Deep convolution neural network is applied to implement pixel-level semantics segmentation of salt seismic images. We increase depth of the network and adds batch normalization and Dropout processing based on U-Net, which makes the neural network model with higher reliability and stronger generalization ability. With experiments, it was found that adding batch normalization layer after convolution layer, and adding Dropout after the pooling layer and concatenate layer improve steadily segmentation performance of the model for salt-body seismic image.

Key words: U-Net, convolution neural network, salt-body classification, image semantics segmentation

CLC Number:

P631.4

LU Xinrui, HUANG Handong, LI Shuai, YIN Long. Salt-body Classification Method Based on U-Net[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2020, 37(3): 327-334.

References

[1] 唐祥华.世界含盐盆地中的油气资源[J]. 中国地质, 1990, (7):27-28.
[2] 王建谋. 盐丘检测[J]. 石油物探译丛, 1993, (2):25-29.
[3] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521:436-444.
[4] ZHOU Y H, LIU H Q, QI P, et al. Forecast of oil production in fractured-vuggy reservoir by using recurrent neural networks[J]. Chinese Journal of Computational Physics, 2018, 35(6):668-674.
[5] ZHANG Q F, YAO J, HUANG Z Q, et al. A multiscale deep learning model for fractured porous media[J]. Chinese Journal of Computational Physics, 2019, 36(6):665-672.
[6] GOU X Y, GUO L X, ZHANG L B. Support vector machine and neural network in inversion of rough surface parameters[J]. Chinese Journal of Computational Physics, 2014, 31(1):75-84.
[7] DI H, WANG Z, ALREGIB G. Deep convolutional neural networks for seismic salt-body delineation[C]. AAPG Annual Convention and Exhibition, 2018.
[8] SHI Yunzhi, WU Xinming, FOMEL Sergey. Automatic salt-body classification using deep-convolutional neural network[C]. SEG Technical Program Expanded Abstracts, 2018.
[9] LECUN Y, BOSER B, DENKER J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4):541-551.
[10] LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time series[J]. The Handbook of Brain Theory and Neural Networks, 1995, 3361(10):1995.
[11] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]. Advances in Neural Information Processing Systems, 2012.
[12] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[13] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778.
[14] RONNEBERGER O, FISCHER P, BROX T. U-net:Convolutional networks for biomedical image segmentation[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2015.
[15] GOODFELLOW I, BENGIO Y, COURVILLE A, et al. Deep learning[M]. Cambridge:MIT Press, 2016.
[16] IOFFE S, SZEGEDY C. Batch normalization:Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.
[17] NOH H, HONG S, HAN B. Learning deconvolution network for semantic segmentation[C]. Proceedings of the IEEE International Conference on Computer Vision, 2015.
[18] SHEKHOVTSOV A, FLACH B. Stochastic normalizations as Bayesian learning[J]. arXiv Preprint arXiv:1811.00639, 2018.
[19] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[20] LI X, CHEN S, HU X, et al. Understanding the disharmony between dropout and batch normalization by variance shift[J]. arXiv Preprint arXiv:1801.05134, 2018.
[21] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. arXiv Preprint arXiv:1207.0580,2012.
[22] ZAGORUYKO S, KOMODAKIS N. Wide residual networks[J]. arXiv Preprint arXiv:1605.07146, 2016.
[23] IGLOVIKOV V, SHVETS A. TernausNet:U-Net with VGG11 encoder pre-trained on imageNet for image segmentation[J]. arXiv Preprint arXiv:1801.05746, 2018.
[24] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012:1097-1105.
[25] KINGMA D P, BA J. Adam:A method for stochastic optimization[J]. arXiv Preprint arXiv:1412.6980, 2014.