A Twice Denoising Autoencoder Framework for Random Seismic Noise Attenuation

Abstract

Many seismic denoising algorithms are easily trapped in the dilemma of signal leaking and noise remaining. But between the two situations, more complex and common situations happens that both signal leaking and noise remaining exist. We have presented a denoising framework for random seismic noise attenuation to mitigate the dilemma and achieve a better denoising performance. The framework consists of a denoising autoencoder (DAE) and data generator. The DAE attenuates random noise without ground truth and works with a vectoring patching technique to reduce time complexity. In the data generator, local correlation is first developed to nonlinear local correlation to detect and extract the signal leakage with noise components suppressed. Then, the extracted signal leakage is compensated back to the DAE output in a supersaturated way to generate a new record. After that, DAE is used again to suppress the remaining noise in the new record. The supersaturated compensation of the first signal leakage counteracts the signal leakage of the second DAE noise attenuation. Formally, the proposed framework can be implemented like a nonlinear geological inversion by thinking of the data generator as the forward operator. Four 2D/3D synthetic and field examples test the performance of the proposed framework. Experiments demonstrate that the proposed method is effective in random noise attenuation and signal leakage compensation, and thus significantly mitigates the mentioned dilemma.