Using the latest results of seismic tomography, we studied the deep tectonic settings of the moderate and strong earthquakes in Anhul Province and its neighboring areas （28° - 39°N, 112°- 124°E）....Using the latest results of seismic tomography, we studied the deep tectonic settings of the moderate and strong earthquakes in Anhul Province and its neighboring areas （28° - 39°N, 112°- 124°E）. The results are as follows： （1） There exists a certain correlation between the location of moderate-strong earthquake, the geologic structure of the surface and the partitioning of active tectonic elements with the upper-crust velocity structure. Most earthquakes recording M ≥ 6.0 occur in high-velocity zones or in the transitional areas between high-velocity and low-velocity zones in the upper crust. Seismicity in the low-velocity zone has a lesser impact. Earthquakes occuring in the high-velocity zone are distributed mainly in the velocity variation area. The boundary belts and the interior of the North China plain fault block are the main active sites of moderate-strong earthquakes. Beneath the fault basins in the western and northern sides of the block, the upper crust is characterized by a wide discontinuous distribution in the low-velocity zone, and in the transition zone from the low- to high velocities, the moderate strong seismicity shows a zonal distribution where active faults are developed. The NW-extension Zhoukou-Hefei-Xuancheng low-velocity zone separates the highvelocity zones of Dabieshan Mountains and west Shandong-Anhul, and moderate-strong earthquakes on its northern side bordering the high-velocity zones are relatively frequent. This low-velocity zone is probably an important and deeply structured boundary between the North China and the South China tectonic provinces. （2） The frequent moderate-strong earthquake recorded in the past and the recent small earthquake activities in the Huoshan-Lu＇ an area are the result of a low-velocity zone in the middle crust beneath the central part of Dabieshan and the two sets of deep faults that cut through the crust. （3） In terms of deep structures, the distribution of moderate-strong earthquake in Anhui Province has an obvious regional 展开更多
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 base...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.展开更多
利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前...利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前人所提出的点云回波强度、归一化强度、最邻近点高差、法向量夹角、X向坡角和Y向坡角等特征的均值和标准差,利用K-最近邻分类法实现单体地物区分的方法。对2010年海地7.0地震震后机载LiDAR数据进行了地面点去除,分别选取了未倒塌建筑物、倒塌建筑物和树木各50个训练样本和各20个测试样本,计算了各因子的分布及其均值和标准差,在分析的基础上最终选取了可分性较强的8个分类特征,利用K-最近邻分类法对测试样本进行了分类,结果显示分类正确率可达85%以上。研究表明选取多个有效的LiDAR点云分类特征可以较好地区分震后未倒塌建筑物、倒塌建筑物和树木,提高震后建筑物震害程度判定的准确性,为应急救援及时提供较为准确的灾情信息支持。展开更多
基金The research was under the key science and technologyresearchfunds of the Earthquake Administration of Anhui Province ,China .
文摘Using the latest results of seismic tomography, we studied the deep tectonic settings of the moderate and strong earthquakes in Anhul Province and its neighboring areas （28° - 39°N, 112°- 124°E）. The results are as follows： （1） There exists a certain correlation between the location of moderate-strong earthquake, the geologic structure of the surface and the partitioning of active tectonic elements with the upper-crust velocity structure. Most earthquakes recording M ≥ 6.0 occur in high-velocity zones or in the transitional areas between high-velocity and low-velocity zones in the upper crust. Seismicity in the low-velocity zone has a lesser impact. Earthquakes occuring in the high-velocity zone are distributed mainly in the velocity variation area. The boundary belts and the interior of the North China plain fault block are the main active sites of moderate-strong earthquakes. Beneath the fault basins in the western and northern sides of the block, the upper crust is characterized by a wide discontinuous distribution in the low-velocity zone, and in the transition zone from the low- to high velocities, the moderate strong seismicity shows a zonal distribution where active faults are developed. The NW-extension Zhoukou-Hefei-Xuancheng low-velocity zone separates the highvelocity zones of Dabieshan Mountains and west Shandong-Anhul, and moderate-strong earthquakes on its northern side bordering the high-velocity zones are relatively frequent. This low-velocity zone is probably an important and deeply structured boundary between the North China and the South China tectonic provinces. （2） The frequent moderate-strong earthquake recorded in the past and the recent small earthquake activities in the Huoshan-Lu＇ an area are the result of a low-velocity zone in the middle crust beneath the central part of Dabieshan and the two sets of deep faults that cut through the crust. （3） In terms of deep structures, the distribution of moderate-strong earthquake in Anhui Province has an obvious regional
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)+1 种基金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).
文摘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.
文摘利用机载激光雷达扫描(Light Detection and Ranging,LiDAR)技术所得点云进行震后倒塌建筑物提取时,树木与倒塌建筑物的点云特征十分相似,较难区分。为了快速准确获取震后房屋建筑物的受损情况,本文提出使用回波次数比特征指标,结合前人所提出的点云回波强度、归一化强度、最邻近点高差、法向量夹角、X向坡角和Y向坡角等特征的均值和标准差,利用K-最近邻分类法实现单体地物区分的方法。对2010年海地7.0地震震后机载LiDAR数据进行了地面点去除,分别选取了未倒塌建筑物、倒塌建筑物和树木各50个训练样本和各20个测试样本,计算了各因子的分布及其均值和标准差,在分析的基础上最终选取了可分性较强的8个分类特征,利用K-最近邻分类法对测试样本进行了分类,结果显示分类正确率可达85%以上。研究表明选取多个有效的LiDAR点云分类特征可以较好地区分震后未倒塌建筑物、倒塌建筑物和树木,提高震后建筑物震害程度判定的准确性,为应急救援及时提供较为准确的灾情信息支持。
