Seismic noise suppression based on convolutional denoising autoencoders
SONG Hui1,2, GAO Yang3, CHEN Wei1,4, ZHANG Xiang1,2
1. Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University), Ministry of Education, Wuhan, Hubei 430100, China; 2. College of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, China; 3. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum(Beijing), Beijing 102249, China; 4. Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Wuhan, Hubei 430100, China
Abstract：Noise attenuation is a long-standing problem in seismic exploration.Traditional denoising me-thods can suppress seismic noise,but they may cause lost effective signals,residual noises and other problems.An unsupervised denoising algorithm based on a convolutional denoising autoencoder is proposed,which can significantly improve the signal-to-noise ratio of seismic data.The algorithm locally and randomly damages seismic data,and then transmits the seismic data damaged to coding and decoding frameworks.The coding framework captures the waveform of the seismic data and eliminates the noise.The decoding framework expands the feature map,recovers the details of the seismic data.Finally,after reconstructing the seismic data,the algorithm trains a model with the error between the reconstructed seismic data and the original seismic data.Considering the complexity of seismic data,a multi-scale convolution module is required to extract the characteristics of seismic data during coding and decoding.Applications to synthetic and real seismic data have proved that the new method is more effective in preserving signals while suppressing noises.Its denoising result is better than a traditional algorithm.
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