| 超参数对GRU-CNN混合深度学习弹性阻抗反演影响研究 |
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| 引用本文： | 梁立锋,刘秀娟,张宏兵,陈程浩,陈锦华.超参数对GRU-CNN混合深度学习弹性阻抗反演影响研究[J].物探与化探,2021(1):133-139. |
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| 作者姓名： | 梁立锋 刘秀娟 张宏兵 陈程浩 陈锦华 |
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| 作者单位： | 岭南师范学院地理系;河海大学地球科学与工程学院 |
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| 基金项目： | 广东省教育厅基金项目(2019KTSCX089);岭南师范学院人才专项(ZL1936);岭南师范学院科研项目(LY1912,LP2036)。 |
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| 摘 要： | CNN-GRU混合深度学习反演弹性阻抗取得了较好的反演效果。但是,基于深度学习的叠前反演参数众多,包括内部深度学习网络可学习参数和外部超参数等,目前超参数选取对网络性能及计算速度影响尚缺乏系统性研究,这直接影响到了该方法的进一步推广应用。因此,本文在混合深度学习反演弹性阻抗基础上,探讨学习率、Epoch、batch_size、正则化参数及参与网络训练的测井个数等5个超参数对网络性能及计算速度的影响,为深度学习地震反演超参数选取提供依据。研究结果可为三维大面积深度学习反演提供一个可行的质控手段,对于推动深度学习方法在石油物探中广泛应用具有一定意义。
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| 关 键 词： | 超参数 门控循环单元 卷积神经网络 混合深度学习 弹性阻抗 |
A study of the effect of hyperparameters GRU-CNN hybrid deep learning EI inversion |
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| LIANG Li-Feng,LIU Xiu-Juan,ZHANG Hong-Bing,CHEN Cheng-Hao,CHEN Jin-Hua.A study of the effect of hyperparameters GRU-CNN hybrid deep learning EI inversion[J].Geophysical and Geochemical Exploration,2021(1):133-139. |
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| Authors: | LIANG Li-Feng LIU Xiu-Juan ZHANG Hong-Bing CHEN Cheng-Hao CHEN Jin-Hua |
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| Institution: | (Department of Geography,Lingnan Normal University,Zhanjiang 524057,China;School of Earth Science and Engineering,Hohai University,Nanjing 210098,China) |
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| Abstract: | Previous studies have shown that CNN-GRU hybrid deep learning inversion EI has the advantages of strong applicability and strong generalization capability.However,there are many pre-stack inversion parameters based on deep learning,such as internal deep learning network learnable parameters and external hyperparameters.At present,there is still no systematic research on the impact of hyperparameter selection on network performance and computing speed,which will directly affect the further promotion and application of the method.Therefore,based on the hybrid deep learning inversion elastic impedance,this paper discusses the impact of five hyperparameters,i.e.,learning rate,Epoch,batch_size,regularization parameter,and the number of wells participating in network training on network performance and calculation speed,thus providing a basis for studying the selection of seismic inversion hyperparameters.The research results can provide a feasible quality control method for three-dimensional large-area deep learning inversion,which is of certain significance for promoting the wide application of deep learning methods in petroleum geophysical prospecting. |
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| Keywords: | super-parameter gate recurrent unit convolutional neural network mixed deep learning elastic impedance |
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