Seismic wave impedance inversion based on the fully convolutional residual shrinkage network
WANG Kang1(), LIU Cai-Yun2(), XIONG Jie1, WANG Yong-Chang1, HU Huan-Fa1, KANG Jia-Shuai1
1. School of Electronics & Information Engineering,Yangtze University,Jingzhou 434023,China 2. School of Information and Mathematics,Yangtze University,Jingzhou 434023,China
Convolutional neural networks(CNNs) have achieved good results in seismic wave impedance inversion,but the inversion accuracy and anti-noise performance need to be improved.Hence,this study proposed a seismic wave impedance inversion method based on the fully convolutional residual shrinkage network with channel-wise thresholds(FCRSN-CW).In this method,the attention mechanism and the soft thresholding were first added to the structure of the residual network to form a inversion network.Then,a synthetic seismic dataset was obtained through forward calculation using wave impedance data.Subsequently,the dataset was applied to train the FCRSN-CW.Finally,the seismic data were put into the trained FCRSN-CW to obtain the inversion results directly.The inversion results of the theoretical model show that the FCRSN-CW can accurately invert the wave impedance and possesses satisfactory learning capacity and anti-noise performance.The inversion results of field data demonstrates that the method based on FCRSN-CW can effectively achieve seismic wave impedance inversion.
王康, 刘彩云, 熊杰, 王永昌, 胡焕发, 康佳帅. 基于全卷积残差收缩网络的地震波阻抗反演[J]. 物探与化探, 2023, 47(6): 1538-1546.
WANG Kang, LIU Cai-Yun, XIONG Jie, WANG Yong-Chang, HU Huan-Fa, KANG Jia-Shuai. Seismic wave impedance inversion based on the fully convolutional residual shrinkage network. Geophysical and Geochemical Exploration, 2023, 47(6): 1538-1546.
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