Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Minerals

Ke Guo¹  Yangqin Guo¹  Huailai Zhou² 

[1] Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Key Laboratory of Earth Exploration and Information Techniques of the Ministry of Education, College of Geophysics, Chengdu University of Technology, Chengdu 610059, China;

关键词: autoencoder convolutional neural network;    noise suppression;    seismic data;    tied weights;    self-supervised learning;   

DOI  :  10.3390/min11101089

来源: DOAJ

【 摘 要 】

Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional autoencoder network based on self-supervised learning was developed herein to attenuate seismic random noise. Unlike conventional methods, our approach did not use synthetic clean data or denoising results as a training label to build the training and test sets. We directly used patches of raw noise data to establish the training set. Subsequently, we designed a robust deep convolutional neural network (CNN), which only depended on the input noise dataset to learn hidden features. The mean square error was then evaluated to establish the cost function. Additionally, tied weights were used to reduce the risk of over-fitting and improve the training speed to tune the network parameters. Finally, we denoised the target work area signals using the trained CNN network. The final denoising result was obtained after patch recombination and inverse operation. Results based on synthetic and real data indicated that the proposed method performs better than other novel denoising methods without loss of signal quality loss.

【 授权许可】

Unknown