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.
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