Seismic wave impedance inversion based on the fully convolutional residual shrinkage network

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.