Deep learning seismic impedance inversion based on prior constraints
SONG Lei1, YIN Xingyao1, ZONG Zhaoyun1, LI Bingkai1, QU Xiaoyang1, XI Xiaoping2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. BGP Geological Research Center, BGP, CNPC, Zhuozhou, Hebei 072751, China
Abstract：We propose a deep learning seismic impedance inversion method based on constraints of prior information. Different from traditional deep learning inversion methods, the inversion area is segmented based on the category of seismic face and segmentation regions are applied as an explicit spatial constraint to constrain the inversion process of the network model. Then the initial model is set as a label to enrich the low-frequency information of the inversion result. Finally, a strong anti-noise activation function is used to improve the adaptability of the network model to noisy data. To reduce the difficulty of acquiring label data and ensure the inversion accuracy of the network, semi-supervised learning is adopted to train the network model. The proposed method is tested on the Marmousi2 model, and the test results indicate that it has a good inversion effect and anti-noise performance. Subsequently, it is successfully applied to the real exploration data of an oilfield.
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