Abstract：Seismic impedance inversion is an important method for reservoir prediction. The accuracy of linear seismic impedance inversion methods depends on the quality of the initial geological model. To get a high-accuracy solution, one can adopt a completely nonlinear method. In view of this, a temporal convolutional neural network (TCN) is first constructed by using a fully convolutional neural network, dilated convolution, causal convolution and a residual block. On this basis, a nonlinear mapping relationship is established between seismic data and wave impedance. Then, samples are trained by the network to yield an inverse mapping model. Further, seismic impedance is obtained by inputting seismic data into the model. According to the test results of forward data and actual data, the method realizes the mapping between seismic data and seismic impedance. It provides an intelligent method with parallel computing power and adaptive structure for seismic impedance inversion and has been applied in sandstone and mudstone reservoir prediction of Gang 2025 Block.
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