Space-varying wavelet extraction method by ConvGRU network with autoencoder-decoder architecture
DAI Yongshou1, LI Honghao1, SUN Weifeng1, SONG Jianguo2, SUN Jiazhao1
1. College of Oceanography and Space Informatics, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China
Abstract：The imaging and inversion quality of seismic exploration is inseparable from the accurate extraction of seismic wavelets. Meanwhile, the dominant frequency and phase of wavelets will change during propagation, causing changes in wavelet morphology. However, existing wavelet estimation methods still lack studies on wavelet spatial variability and rely on prior information such as well logging data. Therefore, this paper proposes a space-varying wavelet extraction method that combines the autoencoder-decoder architecture and ConvGRU network. The method combines convolution operation and gated calculation to extract the main frequency and phase features of wavelets in different traces. Then the features are encoded to obtain the feature variables which can more efficiently extract the features of different traces and different time by the decoder. Finite difference forward modeling and non-stationary convolution model are employed to build training data consistent with actual data distribution. The autoencoder-decoder network model is built and the training data is adopted to train the network and obtain a model for extracting space-varying seismic wavelets. Finally, this model is leveraged to extract multi-trace seismic wavelets. The numerical simulation results show that the proposed method is more accurate than traditional wavelet extraction methods. The processing of actual seismic data in western China proves that the method put forward in this paper is of certain practical application significance.
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