A case-based reasoning method of recognizing liquefaction pits induced by 2021 M_(W) 7.3 Madoi earthquake  

作　　者：Peng Liang Yueren Xu Wenqiao Li Yanbo Zhang Qinjian Tian 

机构地区：[1]Key Laboratory of Earthquake Prediction,Institute of Earthquake Forecasting,China Earthquake Administration,Beijing,100036,China [2]China Earthquake Disaster Prevention Center,China Earthquake Administration,Beijing,100029,China

出　　处：《Earthquake Research Advances》2023年第1期61-69,共9页地震研究进展（英文）

基　　金：Basic Research program from the Institute of Earthquake Forecasting, China Earthquake Administration(Grant No. 2021IEF0505, CEAIEF20220102, and CEAIEF2022050502);high-resolution seismic monitoring and emergency application demonstration (phase Ⅱ)(Grant No. 31-Y30F09-9001-20/22);the National Natural Science Foundation of China (Grant No. 42072248 and 42041006);the National Key Research and Development Program of China (Grant No. 2021YFC3000601-3 and 2019YFE0108900).

摘　　要：Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits based on high-resolution satellite images.Therefore,we provide a framework for extracting liquefaction pits based on a case-based reasoning method.Furthermore,five covariates selection methods were used to filter the 11 covariates that were generated from high-resolution satellite images and digital elevation models(DEM).The proposed method was trained with 450 typical samples which were collected based on visual interpretation,then used the trained case-based reasoning method to identify the liquefaction pits in the whole study area.The performance of the proposed methods was evaluated from three aspects,the prediction accuracies of liquefaction pits based on the validation samples by kappa index,the comparison between the pre-and post-earthquake images,the rationality of spatial distribution of liquefaction pits.The final result shows the importance of covariates ranked by different methods could be different.However,the most important of covariates is consistent.When selecting five most important covariates,the value of kappa index could be about 96%.There also exist clear differences between the pre-and post-earthquake areas that were identified as liquefaction pits.The predicted spatial distribution of liquefaction is also consistent with the formation principle of liquefaction.

关 键 词：Coseismic liquefaction Case-based reasoning K-nearest neighbor Covariates selection 2021 M_(w)7.3 Madoi earthquake Qinghai-Tibetan Plateau 

分 类 号：P315.9[天文地球—地震学]