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Jin Chen, Hong Tang, Wenkai Chen, Naisen Yang. A prediction method of ground motion for regions without available observation data (LGB-FS) and its application to both Yangbi and Maduo earthquakes in 2021. Journal of Earth Science. doi: 10.1007/s12583-021-1560-6
Citation: Jin Chen, Hong Tang, Wenkai Chen, Naisen Yang. A prediction method of ground motion for regions without available observation data (LGB-FS) and its application to both Yangbi and Maduo earthquakes in 2021. Journal of Earth Science. doi: 10.1007/s12583-021-1560-6
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A prediction method of ground motion for regions without available observation data (LGB-FS) and its application to both Yangbi and Maduo earthquakes in 2021

doi: 10.1007/s12583-021-1560-6

  • 1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;

  • 2. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China;

  • 3. Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou 730000, China

  • Received Date: 13 Jul 2021
    Abstract
  • Currently available earthquake attenuation equations are locally applicable, and methods based on observation data are not applicable in areas without available observation data. To solve the above problems and further improve the prediction accuracy of ground motion parameters, we present a prediction model referred to as a light gradient boosting machine with feature selection (LGB-FS). It is based on a light gradient boosting machine (LightGBM) constructed using historical strong motion data from the NGA-west2 database and can quickly simulate the distribution of strong motion near the epicenter after an earthquake. Cases study shows that cmpared with GMPE methods and those based on real-time observation data, the model has a better prediction effect in areas without available observation data and be applied to Yangbi earthquake and Maduo earthquake. The feature importance evaluation based on both information gains and partial dependence plots (PDPs) revealed the complex relationships between multiple factors and ground motion parameters, allowing us to better understand their mechanisms and connections.

     

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