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• W4384303977 abstract "Random noise suppression can greatly improve the signal-to-noise ratio (SNR) of seismic signals. To suppress random seismic noise, we propose an unsupervised deep neural network (UDNN) method based on the local cross-correlation (LCC) loss function and ensemble learning. UDNN with two base learners mainly consists of one input data, two deep neural networks (DNNs), and two output data. The proposed UDNN based on the LCC loss function and ensemble learning (UDNN-LCCEL) method takes full advantage of the nonlinear mapping capabilities of DNNs and maps noisy data into effective noise-free signals by minimizing the total loss function. The total loss function includes the LCC and mean-absolute-error (MAE) loss functions. LCC calculates the local correlation or orthogonalization between output data and the removed noise. By reducing the value of the LCC loss function, we automatically reduce the residual noise and leakage of effective signals in the output data, thus avoiding the overfitting of UDNN. UDNN-LCCEL combines the advantages of two DNNs to obtain ensemble denoised results by ensemble learning. The biggest advantage of our proposed UDNN-LCCEL method is that it does not need to use noise-free data as label data, which can well solve the problem of missing training datasets. The synthetic and field data examples are used to demonstrate that UDNN-LCCEL can achieve good random noise suppression effectiveness." @default.
• W4384303977 created "2023-07-15" @default.
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• W4384303977 date "2023-01-01" @default.
• W4384303977 modified "2023-10-16" @default.
• W4384303977 title "Random Noise Attenuation Using an Unsupervised Deep Neural Network Method Based on Local Orthogonalization and Ensemble Learning" @default.
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• W4384303977 doi "https://doi.org/10.1109/tgrs.2023.3295355" @default.
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