基于时间卷积网络的地震波阻抗反演

王德涛; 陈国雄

doi:10.3799/dqkx.2021.070

Volume 47 Issue 4

Apr. 2022

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2022 > 47(4): 1492-1506

Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

Citation:

Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

Citation:

Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

PDF( 11630 KB)

Seismic Wave Impedance Inversion Based on Temporal Convolutional Network

doi: 10.3799/dqkx.2021.070

Wang Detao^{1, 2
,
,},
Chen Guoxiong^{1
,
,}

1.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
2.
School of Earth Resources, China University of Geosciences, Wuhan 430074, China

Received Date: 2021-02-27
Publish Date: 2022-04-25

Abstract

Abstract

In recent years, the rising of deep learning has significantly boosted the application of artificial intelligence techniques in fields such as seismic data processing, inversion, and interpretation. As a key technology for seismic exploration in the petroleum industry, the precision of seismic wave impedance inversion is essential to characterize hydrocarbon reservoir. A new algorithm is proposed for derivations of wave impedance model from seismic record data using data-driven temporal convolution network (TCN). The proposed algorithm takes the seismic amplitude data as input without relying on the initial inversion model and outputs the impedance information of the subsurface model by utilizing a few well log tag data from the work area and transforming the wave impedance inversion into a time series modeling task. In this paper, the TCN wave impedance inversion model is trained, validated, and tested using the Marmousi2 dataset. The results show high Pearson correlation coefficient (97.92%) and coefficient of determination (95.95%), respectively, on the test set, and also suggest well generalization for predicting wave impedance information far from the training area, and the proposed model significantly out performs previous related work in terms of prediction time and precision. The above results show case the excellent performance of TCN time series model in wave impedance inversion of complex stratigraphic and provide a new idea for seismic wave impedance inversion.
- wave impedance inversion,
- temporal convolutional network,
- deep learning,
- data driven,
- geophysics.

FullText(HTML)

References(34)

References

Alfarraj, M., AlRegib, G., 2018. Petrophysical-Property Estimation from Seismic Data Using Recurrent Neural Networks. SEG Technical Program Expanded Abstracts 2018, 2141-2146. https://doi.org/10.1190/segam2018-2995752.1

Alfarraj, M., Alregib, G., 2019. Semi-supervised Learning for Acoustic Impedance Inversion. SEG Technical Program Expanded Abstracts 2019. https://doi.org/10.1190/segam2019-3215902.1

Alregib, G., Deriche, M., Long, Z. L., et al., 2018. Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective. IEEE Signal Processing Magazine, 35(2): 82-98. https://doi.org/10.1109/MSP.2017.2785979

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

Bai, S. J., Kolter, J.Z., Koltun, V., 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks Forsequence Modeling. arXiv Preprint arXiv: 1803.01271

Bing, P.P., Cao, S.Y., Lu, J.T., 2012. Non-Linear AVO Inversion Based on Support Vector Machine. Chinese Journal of Geophysics, 55(3): 1025-1032(in Chinese with English abstract).

Buland, A., Omre, H., 2003. Bayesian Linearized AVO Inversion. Geophysics, 68(1): 185-198. https://doi.org/10.1190/1.1543206

Calderon-Macias, C., Sen, M.K., 1993. Geophysical Interpretation by Artificial Neural Systems: A Feasibility Study SEG Technical Program Expanded Abstracts 1993. Society of Exploration Geophysicists, 254-257. https://doi.org/10.1190/1.1822453

Das, V., Pollack, A., Wollner, U., et al., 2019. Convolutional Neural Network for Seismic Impedance Inversion. Geophysics, 84(6): R869-R880. https://doi.org/10.1190/geo2018-0838.1

Duijndam, A.J.W., 1988. Bayesian Estimation in Seismic Inversion. Part Ⅰ: Principles. Geophysical Prospecting, 36(8): 878-898. https://doi.org/10.1111/j.1365-2478.1988.tb02198.x

Gholami, A., 2015. Nonlinear Multichannel Impedance Inversion by Total-Variation Regularization. Geophysics, 80(5): R217-R224. https://doi.org/10.1190/geo2015-0004.1

Gu, Y., Zhu, P.M., Rong, H., et al., 2013. Seismic Facies Classification Based on Bayesian Networks. Earth Science, 38(5): 1143-1152(in Chinese with English abstract).

He, K. M., Zhang, X. Y., Ren, S. Q., et al., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on Image Net Classification. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 1026-1034. https://doi.org/10.1109/ICCV.2015.123

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

He, Q.L., Wang, Y.F., 2021. Reparameterized Full-Waveform Inversion Using Deep Neural Networks. Geophysics, 86(1): V1-V13. https://doi.org/10.1190/geo2019-0382.1

Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8): 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

LeCun, Y., Bottou, L., Bengio, Y., et al., 1998. Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11): 2278-2324. https://doi.org/10.1109/5.726791

Li, M., Li, Y., Wu, N., et al., 2020a. Desert Seismic Random Noise Reduction Framework Based on Improved PSO-SVM. Acta Geodaetica et Geophysica, 55(1): 101-117. https://doi.org/10.1007/s40328-019-00283-3

Li, S.C., Liu, B., Ren, Y.X., et al., 2020b. Deep-Learning Inversion of Seismic Data. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 2135-2149. https://doi.org/10.1109/TGRS.2019.2953473

Li, X.G., Wu, X., 2020. Progresses of Artificial Intelligence on Seismic Data Processing and Interpretation Reviewed from SEG Annual Meetings. World Petroleum Industry, 27(4): 27-35(in Chinese with English abstract).

Liu, Z.L., Lu, Z.W., Jia, J.L., et al., 2019. Using Deep Seismic Reflection to Profile Deep Structure of Ore Concentrated Area: Current Status and Case Histories. Earth Science, 44(6): 2084-2105(in Chinese with English abstract).

Martin, G.S., Wiley, R., Marfurt, K.J., 2006. Marmousi2: An Elastic Upgrade for Marmousi. The Leading Edge Nair, 25(2): 156-166. https://doi.org/10.1190/1.2172306

Nair, V., Hinton, G.E., 2010. Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Israel, 807-814.

Puzyrev, V., Egorov, A., Pirogova, A., et al., 2019. Seismic Inversion with Deep Neural Networks: A Feasibility Analysis 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, London, UK, 1-5. https://doi.org/10.3997/2214-4609.201900765

Srivastava, N., Hinton, G., Krizhevsky, A., et al., 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15: 1929-1958.

Tan, F.Q., Li, H.Q., Xu, C.F., et al., 2012. Reservoir Classification of Conglomerate Reservoir Base on Clustering Analysis Method. Progress in Geophysics, 27(1): 246-254(in Chinese with English abstract).

Xu, P., Lu, W., Tang, J., et al., 2019. High-Resolution Reservoir Prediction Using Convolutional Neural Networks 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, London, UK, 1-5. https://doi.org/10.3997/2214-4609.201901392

Yang, P.J., Yin, X.Y., 2008. Prestack Seismic Inversion Method Based on Support Vector Machine. Journal of China University of Petroleum (Edition of Natural Science), 32(1): 37-41(in Chinese with English abstract).

邴萍萍, 曹思远, 路交通, 2012. 基于支持向量机的非线性AVO反演. 地球物理学报, 55(3): 1025-1032. doi: 10.6038/j.issn.0001-5733.2012.03.033

顾元, 朱培民, 荣辉, 等, 2013. 基于贝叶斯网络的地震相分类. 地球科学, 38(5): 1143-1152. doi: 10.3799/dqkx.2013.114

李晓光, 吴潇, 2020. 从SEG年会看人工智能在地震数据处理与解释中的新进展. 世界石油工业, 27(4): 27-35. https://www.cnki.com.cn/Article/CJFDTOTAL-SSYY202004007.htm

刘子龙, 卢占武, 贾君莲, 等, 2019. 利用深地震反射剖面开展矿集区深部结构的探测: 现状与实例. 地球科学, 44(6): 2084-2105. doi: 10.3799/dqkx.2019.020

谭锋奇, 李洪奇, 许长福, 等, 2012. 基于聚类分析方法的砾岩油藏储层类型划分. 地球物理学进展, 27(1): 246-254. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201201028.htm

杨培杰, 印兴耀, 2008. 基于支持向量机的叠前地震反演方法. 中国石油大学学报(自然科学版), 32(1): 37-41. doi: 10.3321/j.issn:1673-5005.2008.01.008

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(16) / Tables(1)

Get Citation

PDF

XML

Article views (3081) PDF downloads(215)

Seismic Wave Impedance Inversion Based on Temporal Convolutional Network

doi: 10.3799/dqkx.2021.070

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content