Wang, Z.F., Cao, H., Yang, Z.F., Xu, H.Q., Yang, M.Q. and Zhao, Y., 2022. Transfer learning seismic impedance inversion method based on temporal convolutional networks. Journal of Seismic Exploration, 31: 391-405. The nonlinear mapping between seismic data and impedance can be established by Temporal Convolutional Networks (TCN), which has been proved by forward modeling data. However, whether the deep neural network can be used to train an inversion mapping model with good generalization ability under a small number of labeled samples remains to be explored. In view of this, the noise analysis of the TCN seismic impedance inversion method was firstly carried out, and the model test showed that the TCN seismic impedance inversion method had certain noise resistance. Secondly, an inversion mapping model was obtained based on the training of Marmousi-2 data set, and then five traces of Overthrust model samples were added for fine-tuning to obtain a new inversion mapping model. The inversion of Overthrust was performed based on the TCN transfer learning inversion mapping model. And the model test results showed that: with a small number of labels, the inversion results of the Overthrust dataset based on TCN transfer learning are higher than the Pearson sum determination results obtained by TCN inversion, and the error profile is relatively small compared to the true impedance. Furthermore, TCN transfer learning method ,which was effectively proved in adjacent blocks of the actual data, compared with the result of the TCN inversion. Therefore, the introduction of transfer learning in TCN seismic impedance inversion can improve the generalization ability of the inversion mapping model trained with a few labeled samples in practical application.