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• W2965510524 abstract "Noise attenuation plays an important role in seismic data processing. We propose a novel denoising method for seismic data based on unsupervised sparse feature learning. Our goal is to obtain the identifiable feature of the noisy seismic data and then to represent the effective signals. By preprocessing the raw data and training the autoencoder neural network with sparse constraint, the sparse feature of the seismic data can be learned and stored in the neural network. We use the adaptive moment estimation as a backpropagation algorithm to minimize the cost function with a sparse penalty term and combine the dropout technique in the training process to improve the feature extraction and generalization capability of the neural network. Then, the test data set can be reconstructed by the most important sparse features. The final denoising result can be obtained by rearranging the output test data set. Compared with three commonly used state-of-the-art denoising methods, the proposed method performs well in applications to denoising for synthetic and real seismic data." @default.
• W2965510524 created "2019-08-13" @default.
• W2965510524 creator A5014653096 @default.
• W2965510524 creator A5018916500 @default.
• W2965510524 creator A5019972279 @default.
• W2965510524 creator A5037374524 @default.
• W2965510524 date "2019-12-01" @default.
• W2965510524 modified "2023-10-05" @default.
• W2965510524 title "Seismic Noise Attenuation Using Unsupervised Sparse Feature Learning" @default.
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• W2965510524 doi "https://doi.org/10.1109/tgrs.2019.2928715" @default.
• W2965510524 hasPublicationYear "2019" @default.
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