PDF
Full
Outline
Ground motion prediction models are an important foundation for seismic hazard analysis. Currently, the research on vertical ground motion prediction models in China is relatively few, and most of the existing ground motion prediction models used parametric equations, which may have limited prediction accuracy. Therefore, the development of horizontal and vertical ground motion prediction models with better prediction accuracy and reliability is necessary for further research. To address the above problems, this study, uses 1991 sets of Chinese horizontal and vertical ground motion records. The Butterworth non-causal filter method is applied to filter and reduce the noise of Chinese ground motion. The Chinese horizontal and vertical ground motion prediction model (CHV-DNN) is developed based on the deep learning method, and it is comprehensively assessed in terms of model performance, physical characteristics, and intra-and inter-event residual analyses. Finally, a correlation coefficient model for Chinese horizontal and vertical ground motion is provided. The results show that based on the residual analysis results of the CHV-DNN model, the most of the inter-event residuals are mainly distributed in the range of [-1, 1], and most of the residuals within events are mainly distributed in the range of [-1.5, 1.5], and the intra-event and inter-event residuals are both uniformly distributed on both sides of the residuals 0 baseline, which validate the reliability and accuracy of the model; The CHV-DNN model has better prediction accuracy and also has well physical characteristics; the correlation coefficient model calculated based on CHV-DNN has been more reasonable. The Chinese horizontal and vertical ground motion prediction model developed in this study will provide a research foundation for horizontal and vertical seismic hazard analysis in China.
PDF
127
65
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
