The lack of low-frequency data in actual seismic data makes the full waveform inversion (FWI) tend to fall into the local minimum,resulting in poor inversion quality and unreliable results.In view of this,data-driven low-frequency recovery mapping was adopted in this study.First,high-pass and low-pass filters were employed to separate high-frequency and low-frequency data from raw data,respectively,and then data preprocessing was carried out.The processed data were used as the training set of the model.Then,the model was built based on the U-Net to establish the mapping relationship between high and low frequencies.To effectively prevent the model from overfitting,the dropout layer and batch processing layer were added based on the U-Net model.Finally,the trained model was used to predict the corresponding low-frequency data from the high-frequency data and conduct inverse data preprocessing.The errors between the predicted low-frequency data after inverse data preprocessing and the real low-frequency data were compared and analyzed,The effectiveness of multi-scale FWI was verified using the depression and Marmousi models.The experimental results show that the average relative errors between the predicted low-frequency data and the real low-frequency data were 5.02% and 13.32%,respectively for training and test data,indicating small errors and high data coincidence.The inversion results of the depression model,the Marmousi model,and actual data show that the prediction of low-frequency data significantly improved the inversion quality and delivered a great performance in the processing of data with much noise.