# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 5 Conclusions  
This paper studied the rationality of using DNNs instead of artificially judging the displacement curve after filtering strong-motion records. The classification performances of the VGG19, ResNet50, InceptionV3, and InceptionResNetV2 deep transfer neural networks only training the fully connected layer and training all the layers were compared and analyzed. The trained model was then applied to high-pass cut-off frequency traverse searching. Additionally, a probability limit of 99.9% was added to the model's classification of the qualified displacement time series. The accuracy of the high-pass cut-off frequency obtained was analyzed by using the high-pass cut-off frequency obtained via the traditional method as the accurate value. Finally, the high-pass cut-off frequencies using the SNR method were also obtained and compared with the results of the proposed method.  
The main conclusions are as follows:  
1. Among the network models that only train the fully connected layer, only the VGG19 model could achieve satisfactory classification performance. In contrast, the rest of the models had higher losses, and the accuracies did not converge. All the trained models of all the network layers could achieve satisfactory classification performance, among which, InceptionResNetV2 had the highest performance with 99.9% and 99.8% accuracy in the training and validation sets, respectively, and 0.003 and 0.009 loss, respectively. The overall accuracy of the test set in the confusion matrix was 96.9%.
2. Considering probability when predicting categories can improve the classification performance of the model. \(R^{2}\) increased by 14.41% on average, and the RMSE, MAE, and MAPE decreased by 12.6%, 16.23%, and 30.01% on average, respectively.
3. The VGG19 model with all the network layers being trained and the addition of probabilistic restrictions in predicting the category was more suitable for the high-pass cut-off frequency automatic search problem of strong-motion records in this paper. The results obtained using this model had the highest \(R^{2}\) of 0.82 and the lowest RMSE, MAE, and MAPE of 0.038, 0.026, and 2.99%, respectively.
4. The high-pass cut-off frequency obtained using the SNR method was generally smaller than the accurate value. This also inspires authors to use this frequency instead of 0.01 Hz as the starting frequency in the search for high-pass cut-off frequencies to improve efficiency.  
Conceptualization, B.L., B.Z., J.K. and X.W.; methodology, B.L., J.K. and C.L.; software, B.L. and X.W.; validation, B.L., B.Z., X.W. and J.K.; formal analysis, B.L. and B.Z.; investigation, B.Z. and C.L.; resources, B.Z. and J.K.; data curation, B.Z. and C.L.; writing--original draft preparation, B.L. and X.W.; writing--review and editing, B.Z., J.K., X.W. and C.L; visualization, B.L.; supervision, J.K.; project administration, C.L.; funding acquisition, B.Z., J.K. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Nos. 2022C05), and the Natural Science Foundation of Heilongjiang Province (LH2022E120) and the National Key Research and Development Program of China (2018 YFE0109800). This support is greatly appreciated.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Grateful acknowledgment is given to [PERSON], [PERSON], [PERSON], and [PERSON] at the Institute of Engineering Mechanics, China Earthquake Administration, for their guidance and suggestions regarding theoretical analyses.