Abstract：For an effective deep learning based seismic impedance inversion strategy, a deep convolutional network is trained by massive data-driven models to obtain the mapping between seismic data and impedance. After the network is pre-trained by substantial synthetic data, a small amount of real data is required for transfer learning of the network. In this paper, we propose a new method based on data augmentation and active learning for seismic wave impedance inversion. First, the original single-trace wave impedance data is augmented by resampling at the same frequency, and then the reflectivity and random kernel are calculated to generate the seismic data after augmentation. The augmented seismic and wave impedance data is taken as training sets, and the maximum-error samples are selected to train the network iteratively considering active learning. The proposed method can avoid seismic wavelet estimation, while training the network with higher accuracy using a small amount of label data. The test results from the Marmousi 2 model demonstrate that this method only needs one tenth of label data and iteration times to achieve the prediction accuracy similar to that of iterative random training, with the prediction errors distributed more evenly on the profile.
伊小蝶, 吴帮玉, 孟德林, 曹相湧. 数据增广和主动学习在波阻抗反演中的应用[J]. 石油地球物理勘探, 2021, 56(4): 707-715.
YI Xiaodie, WU Bangyu, MENG Delin, CAO Xiangyong. Application of data augmentation and active learning to seismic wave impedance inversion. Oil Geophysical Prospecting, 2021, 56(4): 707-715.
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