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An exploration of the use of machine learning to predict lateral spreading
Earthquake Spectra ( IF 5 ) Pub Date : 2021-04-08 , DOI: 10.1177/87552930211004613
Maria Giovanna Durante 1 , Ellen M Rathje 1
Affiliation  

The recent availability of large amounts of high-quality data from post-disaster field reconnaissance enables an exploration of the use of machine learning (ML) approaches to predict earthquake-induced damage. The 2011 Christchurch earthquake in New Zealand caused widespread liquefaction and lateral spreading, and the development of ML models to predict the lateral spreading was enabled by the availability of high-resolution data for lateral spreading displacements, ground shaking, and surface and subsurface features. A dataset of more than 7300 lateral spread observations from a single event in a single geologic setting were used to develop ML classification models using the Random Forest approach for the binary classification problem to identify lateral spread occurrence and a multiclass classification problem to predict the amount of displacement. The best ML models developed in this study accurately predict the lateral spread patterns with an overall accuracy of 80% for the lateral spread occurrence models and 70% for the multiclass displacement classification models. These models show that peak ground acceleration, distance to the river, ground elevation, and groundwater table contribute most to the accuracy of the lateral spread predictions for this dataset, and the inclusion of cone penetration test (CPT) features improves only the prediction of the largest displacement class (>1.0 m). Further research is needed to develop ML models that are generalizable to other earthquake events and geologic settings.



中文翻译:

探索使用机器学习预测横向扩展

最近从灾后现场侦察中获得了大量高质量数据,这使得人们可以探索使用机器学习(ML)方法来预测地震引起的破坏。2011年新西兰克赖斯特彻奇地震引起广泛的液化和横向扩展,并且通过提供横向扩展位移,地面震动以及地表和地下特征的高分辨率数据,可以开发用于预测横向扩展的ML模型。在一个单一的地质环境中,从一个单一事件中收集的超过7300个横向扩展观测值的数据集被用于开发ML分类模型,该模型使用了随机森林方法来解决二元分类问题,以识别横向扩展的发生,并使用多类分类问题来预测横向分布的数量。移位。在这项研究中开发的最好的ML模型可以准确预测横向扩展模式,横向扩展发生模型的整体精度为80%,对于多类位移分类模型,总体精度为70%。这些模型表明,地面加速度峰值,与河流的距离,海拔高度和地下水位对该数据集的横向扩展预测的准确性影响最大,并且包含圆锥体渗透测试(CPT)功能仅改善了最大位移等级(> 1.0 m)的预测。为了进一步发展可推广到其他地震事件和地质环境的ML模型,还需要进一步的研究。

更新日期:2021-04-08
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