Seismic data denoising based on multi-layer perceptron
WANG Qiqi1,2,3, TANG Jingtian1,2,3, ZHANG Liang1,2,3, LIU Xiaojia1,2,3, XU Zhimin4
1. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China; 2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha, Hunan 410083, China; 3. Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Detection, Changsha, Hunan 410083, China; 4. Chengde Petroleum College, Chengde, Hebei 067000, China
Abstract：Seismic exploration has played an important role in tectonic analysis and prospecting of hydrocarbon and other mineral resources.Due to the influence of environment and instruments,seismic data contain random noises,which have a negative impact on processing and interpretation.We propose a multi-layer perceptron (MLP) method to reduce random noises.Seismic data are sampled using a moving window and then converted into a 1D vector,which is utilized as training samples to establish a multi-layer neural network model.The weighting factor of neurons in each layer is calculated using the back propagation algorithm until the mean square training error reaches a minimum.Synthetic or measured noisy seismic data are imported into this established model,and the output is calculated using the weighting factors after training.We compare the denoising results derived from MLP and curvelet methods and conclude that MLP result exhibits higher signal to noise ratio and better signal preservation,especially for structural details.
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