基于BiX-NAS的地震层序智能识别——以荷兰近海地区F3数据为例

陈建玮; 陈国雄; 王德涛; 徐富文

doi:10.3799/dqkx.2023.014

Volume 48 Issue 8

Aug. 2023

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2023 > 48(8): 3162-3178

Chen Jianwei, Chen Guoxiong, Wang Detao, Xu Fuwen, 2023. Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area. Earth Science, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014

Citation:

Chen Jianwei, Chen Guoxiong, Wang Detao, Xu Fuwen, 2023. Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area. Earth Science, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014

Citation:

Chen Jianwei, Chen Guoxiong, Wang Detao, Xu Fuwen, 2023. Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area. Earth Science, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014

PDF( 12655 KB)

Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area

doi: 10.3799/dqkx.2023.014

Chen Jianwei^{1
,
,},
Chen Guoxiong^{1
,
,
,},
Wang Detao¹,
Xu Fuwen²

1.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
2.
The First Geological Brigade of Hubei Geological Bureau, Daye 435000, China

Received Date: 2023-01-11
Publish Date: 2023-08-25

Abstract

Abstract

In recent years, deep learning methods have been widely focused and applied in the field of seismic data processing and interpretation, where most deep learning algorithms employ end-to-end deep convolutional neural networks for the extraction and identification of geological features (e.g., stratum, fault, and salt dome). However, these algorithms often contain hundreds of thousands or even millions of trainable parameters, which lead to the model of parameter redundancy and low training efficiency. Therefore, a lightweight bi-directional multi-scale network is constructed for the intelligent identification of stratum. Specifically, the model eliminates the obvious redundant connections of the bi-directional multi-scale network structure through the two-stage Neural Architecture Search (NAS), which greatly simplifies the network structure, reduces the parameter redundancy, and improves the training efficiency. The Netherlands F3 dataset was used to train, verify and predict the simplified bi-directional multi-scale network by the NAS. The results show that the average recognition accuracy of the lightweight model reaches 95.52% in the actual stratigraphic identification task, and it has well generalization to the prediction work area far from the training work area. In addition, the number of parameters of the proposed model is only 4.4% of the U-shaped convolutional neural network (U-Net), and which outperforms the previous related work in terms of training efficiency and the number of model parameters. It is also robust when processing noisy seismic data. Therefore, the BiX-NAS network model has good prospects for application in practical automatic seismic stratigraphic identification.
- stratigraphic identification,
- deep learning,
- neural architecture search,
- bi-directional multi-scale network

FullText(HTML)

References(59)

References

Abdellatif, A., Elsheikh, A.H., Graham, G., et al., 2022. Generating Unrepresented Proportions of Geological Facies using Generative Adversarial Networks. Computers & Geosciences, 162: 105085. https://doi.org/10.1016/j.cageo.2022.105085

Araya-Polo, M., Jennings, J., Adler, A., et al., 2018. Deep-Learning Tomography. The Leading Edge, 37(1): 58-66. https://doi.org/10.1190/tle37010058.1

Badrinarayanan, V., Kendall, A., Cipolla, R., 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615

Bahorich, M., Farmer, S., 1995. 3-D Seismic Discontinuity for Faults and Stratigraphic Features: The Coherence Cube. The Leading Edge, 14(10): 1053-1058. https://doi.org/10.1190/1.1437077

Cao, S., 2019. Application of Geostatistical Inversion Method in Reservoir Prediction of Coal Measure Strata in Hangjinqi Area. Petroleum Geology and engineering, 33(5): 41-44. (in Chinese with English abstract)

Ciresan, D., Giusti, A., Gambardella, L., et al., 2012. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. Advances in Neural Information Processing Systems, 25: 2843-2851.

Di, H., Wang, Z., Alregib, G., 2018. Deep Convolutional Neural Networks for Seismic Salt-Body Delineation. In: 2018 AAPG Annual Convention and Exhibition, Search and Discovery Article, 90323.

Donald, A. S., 2021. How Deep Learning Networks could be Designed to Locate Mineral Deposits. Journal of Earth Science, 32(2): 288-292. https://doi.org/10.1007/s12583-020-1399-2

Dorn, G.A., 1998. Modern 3-D Seismic Interpretation. The Leading Edge, 17(9): 1262-1262. https://doi.org/10.1190/1.1438121

Du, G., Cao, X., Liang, J., et al., 2020. Medical Image Segmentation Based on U-Net: A Review. Journal of Imaging Science and Technology, 64(2): 20508-1-20508-12. https://doi.org/10.2352/J.ImagingSci.Technol.2020.64.2.020508

Fan, Y., Huang, L., Dai, S., 1999. Application of Crossplot Technique to the Determination of Lithology Composition and Fracture Identification of Igneous Rock. Well Logging Technology, 23(1): 53-56(in Chinese with English abstract).

Gao, K., Huang, L., Zheng, Y., et al., 2022. Automatic Fault Detection on Seismic Images Using a Multiscale Attention Convolutional Neural Network. Geophysics, 87(1): N13-N29. https://doi.org/10.1190/geo2020-0945.1

Guo, Z., Zhang, X., Mu, H., et al., 2020. Single Path One-Shot Neural Architecture Search with Uniform Sampling. In: 2020 European Conference on Computer Vision(ECCV), Springer, Cham, 544-560. https://doi.org/10.1007/978-3-030-58517-4_32

He, K., Zhang, X., Ren, S., et al., 2016. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, USA, 770-778. https://doi.org/10.1109/CVPR.2016.90

Ioffe, S., Szegedy, C., 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: 2015 International Conference on Machine Learning(ICML), PMLR, 448-456.

Jang, E., Gu, S., Poole, B., 2017. Categorical Reparameterization with Gumbel-Softmax. In: 2017 International Conference on Learning Representations(ICLR). https://doi.org/10.48550/arXiv.1611.01144

Kingma, D.P., Ba, J., 2014. Adam: A Method for Stochastic Optimization. Computer Science, 14(6): 123-126. https://doi.org/10.48550/arXiv.1412.6980

Laloy, E., Hérault, R., Lee, J., et al., 2017. Inversion Using a New Low-Dimensional Representation of Complex Binary Geological Media Based on a Deep Neural Network. Advances in Water Resources, 110: 387-405. https://doi.org/10.1016/j.advwatres.2017.09.029

Laloy, E., Hérault, R., Jacques, D., et al., 2018. Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network. Water Resources Research, 54(1): 381-406. https://doi.org/10.1002/2017WR022148

Liu, H., Simonyan, K., Yang, Y., 2018. DARTS: Differentiable Architecture Search. In: 2018 International Conference on Learning Representations(ICLR). https://doi.org/10.48550/arXiv.1806.09055

Long, J., Shelhamer, E., Darrell, T., 2015. Fully Convolutional Networks for Semantic Segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 3431-3440. https://doi.org/10.1109/CVPR.2015.7298965

Lopez-Alvis, J., Laloy, E., Nguyen, F., et al., 2021. Deep Generative Models in Inversion: a Review and Development of a New Approach Based on a Variational Autoencoder. Computers & Geosciences, 152: 104762. https://doi.org/10.1016/j.cageo.2021.104762

Ma, G., Wu, Q., Xiong, S., et al., 2021. Ratio Method for Calculating the Source Location of Gravity and Magnetic Anomalies Based on Deep Learning. Earth Science, 46(9): 3365-3375. https://doi.org/10.3799/dqkx.2020.350 (in Chinese with English abstract)

Marfurt, K.J., Kirlin, R.L., Farmer, S.L., et al., 1998. 3-D Seismic Attributes Using a Semblance-Based Coherency Algorithm. Geophysics, 63(4): 1150-1165. https://doi.org/10.1190/1.1444415

Mosser, L., Dubrule, O., Blunt, M., 2018. Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks As A Geological Prior. Mathematical Geosciences, 52(1), 53-79. https://doi.org/10.3997/2214-4609.201803018

Nair, V., Hinton, G.E., 2010. Rectified Linear Units Improve Restricted Boltzmann Machines. In: 2010 International Conference on Machine Learning(ICML), Haifa, Israel, 807-814.

Qi, J., Lyu, B., Wu, X., et al., 2020. Comparing Convolutional Neural Networking and Image Processing Seismic Fault Detection Methods. In: 2020 SEG International Exposition and Annual Meeting. OnePetro, 1111-1115. https://doi.org/10.1190/segam2020-3428171.1

Real, E., Moore, S., Selle, A., et al., 2017. Large-Scale Evolution of Image Classifiers. In: 2017 International Conference on Machine Learning(ICML), PMLR, 2902-2911.

Real, E., Aggarwal, A., Huang, Y., et al., 2019. Regularized Evolution for Image Classifier Architecture Search. In: 2019 AAAI Conference on Artificial Intelligence 33: 4780-4789. https://doi.org/10.1609/aaai.v33i01.33014780

Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention, 9351: 234-241. https://doi.org/10.1007/978-3-319-24574-4_28

Shi, Y., Wu, X., Fomel, S., 2019. SaltSeg: Automatic 3D Salt Segmentation Using a Deep Convolutional Neural Network. Interpretation, 7(3), SE113-SE122. https://doi.org/10.1190/int-2018-0235.1

Silva, R.M., Baroni, L., Ferreira, R.S., et al., 2019. Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation. arXiv preprint, 2019(6): 38-42. https://doi.org/10.48550/arXiv.1904.00770

Tao, H., Cheng, R., Zhao, X., et al., 2011. Well Logging Response to the Volcaniclastic Rocks of Hailar Basin and Application. Chinese Journal of geophysics, 54(2): 534-544. https://doi.org/10.3969/j.issn.0001-5733. 2011. 02.033(in Chinese with English abstract) doi: 10.3969/j.issn.0001-5733.2011.02.033

Wang, D., Chen, G., 2021. Seismic Stratum Segmentation Using an Encoder-Decoder Convolutional Neural Network. Mathematical Geosciences, 53(6): 1355-1374. https://doi.org/10.1007/s11004-020-09916-8

Wang, X., Xiang, T., Zhang, C., et al., 2021. BiX-NAS: Searching Efficient Bi-Directional Architecture for Medical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention, 229-238. https://doi.org/10.1007/978-3-030-87193-2_22

Wang, D., Chen, G., 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506(in Chinese with English abstract).

Wu, X., Shi, Y., Fomel, S., et al., 2018. Convolutional Neural Networks for Fault Interpretation in Seismic Images. In: 2018 SEG International Exposition and Annual Meeting. OnePetro: Anaheim, California; 1946-1950. https://doi.org/10.1190/segam2018-2995341.1

Wu, H., Zhang, B., Lin, T., et al., 2019a. Semiautomated Seismic Horizon Interpretation Using the Encoder-Decoder Convolutional Neural Network. Geophysics, 84(6): B403-B417. https://doi.org/10.1190/geo2018-0672.1

Wu, X., Liang, L., Shi, Y., et al., 2019b. FaultSeg3D: Using Synthetic Data Sets to Train an End-to-End Convolutional Neural Network for 3D Seismic Fault Segmentation. Geophysics, 84: IM35-IM45. https://doi.org/10.1190/geo2018-0646.1

Wu, X., Liang, L., Shi, Y., et al., 2019c. Multitask Learning for Local Seismic Image Processing: Fault Detection, Structure-Oriented Smoothing with Edge-Preserving, and Seismic Normal Estimation by Using a Single Convolutional Neural Network. Geophysical Journal International, 219(3): 2097-2109. https://doi.org/10.1093/gji/ggz418

Xiang, T., Zhang, C., Liu, D., et al., 2020. BiO-Net: Learning Recurrent Bi-Directional Connections for Encoder-Decoder Architecture. Medical Image Computing and Computer: Assisted Intervention, 74-84. https://doi.org/10.1007/978-3-030-59710-8_8

Xue, W., Chen, B., Zhang, Z., 2019. Recognition of Stratigraphic Lithology by BP-Neural Network-A Case Study of Yiner Basin. Pretrochemical Technology, 26(11): 103-107. https://doi.org/CNKI:SUN:SHJS.0.2019-11-058 (in Chinese with English abstract)

Yang, F., Ma, J., 2019. Deep-Learning Inversion: a Next Generation Seismic Velocity-Model Building Method. Geophysics, 84(4): R583-R599. https://doi.org/10.1190/GEO2018-0249.1

Yang, D., Cai, Y., Hu, G., et al., 2020a. Seismic Fault Detection Based on 3D Unet++ Model. In: 2020 SEG International Exposition and Annual Meeting. OnePetro, 1631-1635. https://doi.org/10.1190/segam2020-3426516.1

Yang, Z., Wang, Y., Chen, X., et al. 2020b. CARS: Continuous Evolution for Efficient Neural Architecture Search. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 1829-1838. https://doi.org/10.48550/arXiv.1909.04977

Yu, S., Ma, J., 2021. Deep Learning for Geophysics: Current and Future Trends. Reviews of Geophysics, 59(3), e2021RG000742. https://doi.org/10.1029/2021RG000742

Zeng, Y., Jiang, K., Chen, J., 2018. Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks. In: 2019 International Conference on Information System and Data Mining, 16-20. https://doi.org/10.1145/3325917.3325926

Zhang, H., Liu, Y., Zhang, Y., et al., 2019. Automatic Seismic Facies Interpretation Based on an Enhanced Encoder-Decoder Structure. In: 2019 SEG Technical Program Expanded Abstracts, 2408-2412. https://doi.org/10.1190/segam2019-3215516.1

Zhang, H., Chen, T., Liu, Y., et al., 2021. Automatic Seismic Facies Interpretation Using Supervised Deep Learning. Geophysics, 86(1): IM15-IM33. https://doi.org/10.1190/geo2019-0425.1

Zheng, Y., Li, G., 2009. Application of Support Vector Machine to Stratum Recognition. Journal of Henan Normal University(Natural Science), 37(2): 37-39(in Chinese with English abstract).

Zheng, Z.H., Kavousi, P., Di, H.B., 2014. Multi-Attributes and Neural Network-Based Fault Detection in 3D Seismic Interpretation. Advanced Materials Research, 838-841: 1497-1502. https://doi.org/10.4028/www.scientific.net/AMR.838-841.1497

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., et al., 2018. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In: 2018 Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 3-11. https://doi.org/10.1007/978-3-030-00889-5_1

曹绍贺, 2019. 地质统计学反演方法在杭锦旗区块煤系地层储层预测中的应用. 石油地质与工程, 33(5): 4. https://www.cnki.com.cn/Article/CJFDTOTAL-SYHN201905010.htm

范宜仁, 黄隆基, 代诗华, 1999. 交会图技术在火山岩岩性与裂缝识别中的应用. 测井技术, 23(1): 53-56. https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS901.012.htm

马国庆, 吴琪, 熊盛青, 等, 2021. 基于重磁数据梯度比值的深度学习技术实现场源位置反演方法. 地球科学, 46(9): 3365-3375. doi: 10.3799/dqkx.2020.350

陶宏根, 程日辉, 赵小青, 等, 2011. 海拉尔盆地火山碎屑岩的测井响应与应用. 地球物理学报, (2): 534-544. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201102035.htm

王德涛, 陈国雄, 2022. 基于时间卷积网络的地震波阻抗反演. 地球科学, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

薛文卓, 陈彪, 张哲豪, 2019. BP-神经网络识别地层岩性-以银额盆地为例. 石化技术, (11): 103-107. https://www.cnki.com.cn/Article/CJFDTOTAL-SHJS201911058.htm

郑延斌, 李国和, 2009. 支持向量机在地层识别中的应用. 河南师范大学学报(自然科学版), (2): 37-39. https://www.cnki.com.cn/Article/CJFDTOTAL-HNSX200902011.htm

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(18) / Tables(6)

Get Citation

PDF

XML

Article views (2212) PDF downloads(184)

Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area

doi: 10.3799/dqkx.2023.014

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content