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| A study of the effect of hyperparameters GRU-CNN hybrid deep learning EI inversion |
LIANG Li-Feng1( ), LIU Xiu-Juan1(), ZHANG Hong-Bing2, CHEN Cheng-Hao1, CHEN Jin-Hua1 |
1. Department of Geography,Lingnan Normal University,Zhanjiang 524057,China
2. 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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Received: 02 January 2020
Published: 01 March 2021
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Corresponding Authors:
LIU Xiu-Juan
E-mail: 121436068@qq.com;544022065@qq.com
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Time domain seismic profile of marmorsi2 model(local)
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Comparison of inversion efficiency and result between convolution deep learning and hybrid deep learning
a—inversion results comparison;b—time consmption comparison;c—correlation coefficient comparison
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Batch-size effect on correlation coefficient(a) and calculation time(b)
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Influence of number of wells participating in the training on correlation coefficient(a) and calculation time(b)
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Comparison of operation time between GPU and CPU with the same hyper-parameters
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Relation diagram of Epoch vs the correlation coefficient(a) and time consumption(b)
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Relation diagram of regularization parameter α vs correlation coefficient(a) and time consumption(b)
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Learning rate curve and improvement method
a—cosinc learning rate curve;b—loss curve comparison between constant learning rate and cosinc learning rate
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