提出了基于强震动记录H/V谱比曲线的场地分类新方法，同时兼顾场地卓越周期、谱比幅值和谱比曲线形状特征，引入熵权决策理论进行谱比曲线自动识别匹配，较以往方法有效提升了分类准确率；给出了适用于我国场地I、II、III类的经验谱比曲线，划分了我国强震动观测台网台站的场地类别，解决了我国大量强震动记录由于场地信息缺失而合理使用受制约的问题，也可为我国强震动Flatfile的场地信息识别提供一条有效途径。
提出了利用强震动记录H/V谱比识别场地发生非线性反应的ADNL、RFp等指标，同时引入聚类人工智能算法，可有效揭示场地非线性反应特性与规律，在2008年汶川地震、2016年新西兰凯库拉地震及2018年日本北海道地震得到实践应用；发展基于谱反演技术的台站场地线性与非线性反应识别技术，从场地角度解释地表地震动及震害分布特征。

图1 基于强震动记录H/V谱比曲线的场地分类方法流程以及我国场地经验H/V谱比曲线

图2 利用聚类人工智能算法自动识别日本2018年北海道地震中强震动台站的场地线性及非线性反应

成果目录：

  1. Yuting Zhang, Yefei Ren, Ruizhi Wen, Hongwei Wang and Kun Ji. Regional terrain-based VS30 prediction models for China. Earth, Planets and Space, 2023 75:72.
  2. Kun Ji, Chuanbin Zhu, Yaghmaei-Sabegh Saman, Jianqi Lu, Yefei Ren, Ruizhi Wen. Site classification using deep-learning-based image recognition techniques. Earthquake Engineering & Structural Dynamics, 2022, Published online.
  3. 任叶飞, 刘也, 张鹏, 冀昆, 王宏伟, 温瑞智, 谢俊举. 基于聚类分析的我国工程场地分类方案优化研究. 建筑结构学报. 2022, 在线出版.
  4. 王大任, 任叶飞, 张雨婷, 冀昆, 王宏伟, 温瑞智. 一种建筑工程场地参数VS30的外推模型修正方法. 哈尔滨工业大学学报, 2022, 在线出版.
  5. Kun Ji, Yefei Ren, Ruizhi Wen, ChuanBin Zhu, Ye Liu and Baofeng Zhou. HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations. Journal of Earthquake Engineering, 2022, 26(16): 8423–8445.
  6. 刘也, 任叶飞, 王大任, 王宏伟, 冀昆, 温瑞智, 周宝峰. 基于地震动预测残差分析的工程场地分类标准检验与评价. 工程力学, 2023, 40(6): 99-109.
  7. 张雨婷, 任叶飞, 温瑞智, 王大任, 冀昆. 基于决策树考虑地形特征的场地参数估计方法. 地球物理学报, 2022, 65(2): 698-710.
  8. Hongwei Wang, Chunguo Li, Ruizhi Wen and Yefei Ren. Integrating Effects of Source‐Dependent Factors on Sediment‐Depth Scaling of Additional Site Amplification to Ground‐Motion Prediction Equation. Bulletin of the Seismological Society of America, 2022, 112(1): 400-418.
  9. Ying Zhou, Hongwei Wang, Ruizhi Wen, Yefei Ren, Kun Ji. Insights on nonlinear soil behavior and its variation with time at strong-motion stations during the Mw7.8 Kaikōura, New Zealand earthquake. Soil Dynamics and Earthquake Engineering, 2020, 136, paper ID: 106215.
  10. Kun Ji, Ruizhi Wen,Yefei Ren, Yadab P. Dhakal. Nonlinear seismic site response classification using K-means clustering algorithm: Case study of the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake, Japan. Soil Dynamics and Earthquake Engineering, 2020, 128, paper ID: 105907.
  11. Kun Ji, Yefei Ren, Ruizhi Wen. Site classification for National Strong Motion Observation Network System (NSMONS) stations in China using an empirical H/V spectral ratio method. Journal of Asian Earth Sciences, 2017, 147: 79–94.
  12. Yefei Ren, RuizhiWen, Xinxin Yao and Kun Ji. Five parameters for the evaluation of the soil nonlinearity during the Ms8.0 Wenchuan Earthquake using the HVSR method. Earth, Planets and Space, 2017, 69, paper no. 116, 1-17.
  13. 温瑞智, 冀昆, 任叶飞, 王宏伟. 基于谱比法的我国强震台站场地分类. 岩石力学与工程学报, 2015, 34(6): 1236-1241.
  14. Ruizhi Wen, Yefei Ren, Zhenghua Zhou, Xiaojun Li. Temporary strong-motion observation network for Wenchuan aftershocks and site classification. Engineering Geology, 2014, 180: 130-144.
  15. Ren Yefei, Wen Ruizhi, Hiroaki Yamanaka and Toshihide Kashima. Site effects by generalized inversion technique using strong motion recordings of the 2008 Wenchuan earthquake. Earthquake Engineering and Engineering Vibration, 2013, 12(2): 165-184.
  16. 任叶飞, 温瑞智, 山中浩明, 鹿嶋俊英. 运用广义反演法研究汶川地震场地效应. 土木工程学报, 2013, 46(S2): 146-151.
  17. 温瑞智, 任叶飞, 齐文浩, 卢滔, 杨振宇, 单振东, 汪云龙. 2013年4月20日芦山地震最大加速度记录分析. 西南交通大学学报, 2013, 48(5): 783-791.
  18. Wen Ruizhi, Ren Yefei, and Shi Dacheng. Improved HVSR site classification method for free-field strong motion stations validated with Wenchuan aftershock recordings. Earthquake Engineering and Engineering Vibration, 2011, 10(3): 325-337.