Intelligent seismic facies classification based on an improved deep learning method
YAN Xingyu1,2, GU Hanming1,2, LUO Hongmei3, YAN Youping4
1. Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan, Hubei 430074, China; 2. Hubei Key Labrotary for Subsurface Multi-scale Imaging, China University of Geosciences, Wuhan, Hubei 430074, China; 3. Research Institute of Exploration & Development, Sinopec Shengli Oilfield Comopany, Dong-ying, Shandong 257015, China; 4. North China Branch, Sinopec Engineering Geophysics Co., Ltd., Zhengzhou, Henan 450000, China
Abstract：Intelligent seismic facies classification based on deep learning can greatly reduce manual operations.However,when using conventional deep learning methods for seismic facies recognition,the network model can only extract the feature map on a single receptive field,and it is difficult to obtain the global spatial distribution on seismic sections.In addition,the prediction of the boundary of minor seismic facies is inaccurate,and there is not a method for assessing the reliability of prediction on multi-class segmentation models.We propose a facies classification network by simplified U-Net with Pyramid Pooling Module which has been empirically proved to be an effective global contextual prior.And an objective function combining cross-entropy and Dice loss is adopted to improve the boundary characterization of minor seismic facies in unbalanced data.We present Prediction Entropy for estimating the uncertainty of classification results.Applied to F3 dataset,the improved method can enhance prediction accuracy and boundary characterization,and the index of Prediction Entropy can evaluate the uncertainty of the prediction results.
闫星宇, 顾汉明, 罗红梅, 闫有平. 基于改进深度学习方法的地震相智能识别[J]. 石油地球物理勘探, 2020, 55(6): 1169-1177.
YAN Xingyu, GU Hanming, LUO Hongmei, YAN Youping. Intelligent seismic facies classification based on an improved deep learning method. Oil Geophysical Prospecting, 2020, 55(6): 1169-1177.
朱剑兵,赵培坤.国外地震相划分技术研究新进展[J].勘探地球物理进展,2009,32(3):167-171.ZHU Jianbing,ZHAO Peikun.Advances in seismic facies classification technology abroad[J].Progress in Exploration Geophysics,2009,32(3):167-171.
[2]
唐金炎,杜品,陈智雍.地震几何属性参数在地震相识别中的应用[J].油气地球物理,2011,9(1):34-35.TANG Jinyan,DU Pin,CHEN Zhiyong. Application of the seismic geometric attributes parameters in seismic facies identification[J].Petroleum Geophysics,2011,9(1):34-35.
[3]
张林科,覃丽君,张国焘.基于地震属性的地震相分析思路[J].工程地球物理学报,2010,7(6):694-698.ZHANG Linke,QIN Lijun,ZHANG Guotao.On seismic facies analysis based on seismic attributes[J].Chinese Journal of Engineering Geophysics,2010,7(6):694-698.
[4]
段玉顺,李芳.地震相的自动识别方法及应用[J].石油地球物理勘探,2004,39(2):158-162.DUAN Yushun,LI Fang.Automatic identification of seismic facies and application[J].Oil Geophysical Prospecting,2004,39(2):158-162.
[5]
Jesper S.Deep learning seismic facies on state-of-the-art CNN architectures transfer learning seismic facies[C].SEG Technical Program Expanded Abstracts,2018,37:2036-2040.
[6]
Zhao T.Seismic facies classification using different deep convolutional neural networks[C].SEG Technical Program Expanded Abstracts,2018,37:2046-2050.
[7]
Lei L,Yu L,Xiong Z,et al.Convolutional recurrent neural networks based waveform classification in seismic facies analysis[C].SEG Technical Program Expanded Abstracts,2019,38:2599-2603.
[8]
Qi J,Ha T,Lyu B,et al.Seismic attribute selection for machine learning based facies analysis choosing the best seismic attributes[C].SEG Technical Program Expanded Abstracts,2019,38:1853-1857.
[9]
Wu X,Shi Y,Fomel S,et al.Convolutional neural net-works for fault interpretation in seismic images[C].SEG Technical Program Expanded Abstracts,2018,37:1946-1950.
[10]
Shi Y,Wu X,Fomel S.Automatic salt-body classification using deep-convolutional neural network[C].SEG Technical Program Expanded Abstracts,2018,37:1971-1975.
[11]
Alaudah Y,Alfarraj M.Structure label prediction using similarity-based retrieval and weakly supervised label mapping[J].Geophysics,2019,84(1):V67-V79.
[12]
Mukhopadhyay P,Mallick S.Bayesian deep learning for seismic facies classification and its uncertainty estimation[C].SEG Technical Program Expanded Abstracts,2019,38:2488-2492.
[13]
Zhang H,Liu Y,Zhang Y,et al.Automatic seismic facies interpretation based on an enhanced encoder-decoder structure[C].SEG Technical Program Expanded Abstracts,2019,38:2408-2412.
[14]
Zhao H,Shi J,Qi X,et al.Pyramid scene parsing network[C].Proceedings IEEE Conference on Computer Vision and Pattern Recognition,2017,6230-6239.
[15]
Panchapagesan S,Sun M,Khare A,et al.Multi-task learning and weighted cross-entropy for DNN-based keyword spotting[M].Interspeech,2016,P760-P764.
[16]
Sudre C H,Li W,Vercauteren T,et al.Generalized Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations,Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support[M].Springer,Cham,2017,240-248.
[17]
Alaudah Y,Michalowicz P,Alfarraj M,et al.A machine-learning benchmark for facies classification[J].Interpretation,2019,7(3):SE175-SE187.
[18]
Kingma D P,Ba J.Adam:A Method for Stochastic Optimization[C].The 3rd International Conference for Learning Representations,2015,http://arxiv.org/abs/1412.6980.
[19]
闫星宇,顾汉明,肖逸飞,等.XGBoost算法在致密砂岩气储层测井解释中的应用[J].石油地球物理勘探,2019,54(2):447-455.YAN Xingyu,GU Hanming,XIAO Yifei,et al.XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data[J].Oil Geophysical Prospecting,2019,54(2):447-455.
[20]
Shannon,Claude E.A mathematical theory of communication[J].Bell System Technical Journal,1948,27(3):379-423.
