Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation
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摘要: 为了提高建筑物震害信息提取的效率与准确度,针对震后高分辨率遥感影像,根据震害建筑物在遥感影像上的特征,以2010年海地MS7.0地震为例,通过尺度参数估计算法自动选择最优分割尺度对影像进行多尺度分割,并采用面向对象方法对海地高分辨率遥感影像进行建筑物震害信息提取,同时与基于像元的支持向量机、反向传播神经网络、基于分类回归算法的决策树分类方法进行比较。试验结果表明,面向对象的分类方法具有更好的目视效果和更高的分类精度,有利于地震后震害信息的准确提取和快速评估。Abstract: In order to improve the efficiency and the accuracy of information extraction about earthquake damage building, based on high resolution remote sensing image after the earthquake, and according to the features of earthquake damage buildings in remote sensing images, we took a case study of MS7.0 Haiti earthquake in 2010, through the ESP algorithm automatically chose the optimal segmentation scale to multi-scale segmentation of images, used object-oriented method to Haiti high-resolution remote sensing image information extraction of earthquake damage buildings. At the same time, it is compared with Support Vector Machine based on pixel, BP neural network and Decision Tree classification method based on CART algorithm, the experimental results show that the object-oriented classification method has better visual effect and higher classification accuracy, which is beneficial to the accurate extraction and rapid evaluation of earthquake damage information after the earthquake.
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引言
随着城镇化建设的发展,土地资源日渐紧缺,对地下空间的开发和利用不断深化。然而,地下结构的建设破坏了土体的局部整体性,改变了原场地的动力特性,因而对附近地上和地下工程结构的抗震安全造成了影响。因此,系统深入地分析由于地下结构的存在而引起的沿线地震动随深度的变化规律,对地下结构沿线的结构抗震设计具有十分重要的意义。
地下结构对附近场地地震动影响的实质是地下结构对地震波的散射。Mow和Pao (1973)最早运用波函数展开法研究了无限空间中的隧道在弹性波入射下的动应力集中问题;随之,Lee和Trifunac (1979)运用该方法分析了半空间中衬砌隧道对SH波的动力响应。Lee和Karl (1992,1993)通过理论分析给出了半空间中单个无衬砌隧道对P波和SV波散射问题的解析解。基于单个衬砌隧道的研究,Liang等(2003)得到了半空间中双衬砌隧道对P波和SV波散射问题的解析解。Liu等(2016)分析了弹性半空间中平面波作用下双垂直衬砌隧道的动力相互作用。Xu等(2011)采用傅里叶-贝塞尔级数展开方法计算了半空间中圆形衬砌隧道对P波入射的动力响应。考虑不同种类弹性波的入射情形,梁建文等(2005a,b)研究了地下圆形隧道对地表运动幅值的影响。Liu等(2013)考察了弹性半空间中隧道处于浅埋时平面 P-SV 波和瑞雷波的动力响应。Luco和de Barros (2010)以及de Barros和Luco (2010)计算了水平层状半空间中埋置隧道在入射体波下的三维动力响应。对于平面SV波和P波垂直入射的情形,Oliaei和Alitalesh (2015)分析了由于地下圆形和椭圆形隧道的存在而引起的地面位移被放大的现象。利用四阶有限差分方法,Narayan等(2015)探讨了瑞雷波入射下地下无衬砌隧道和有衬砌隧道对其周围应变和黏弹性地基地表位移的影响。Alielahi和Adampira (2016)应用边界元法给出了P波和SV波入射时双平行隧道对其周围垂直平面内地震动响应的影响。Liu和Liu (2015)利用间接边界元法讨论了弹性楔形空间中隧道在SH波入射下对附近表面地震动的影响。Parvanova等(2014)利用数值模拟方法探讨了局部地形对隧道动力响应的影响。
目前,大部分研究成果仅针对SH波入射情况下地下隧道对地表面地震动的影响(Liang et al,2012,2013;付佳等,2016),而且对考虑地下隧道周围土体在一定深度范围内的动力响应研究也较少。为此,本文拟以含有圆形衬砌隧道的弹性半空间为研究对象,分析地下圆形隧道对场地动力响应的影响,重点研究隧道埋深、衬砌刚度、入射角度以及入射频率对地下隧道周围土体位移振动幅值随深度的变化规律,以期为定量评估地下隧道对既有地下建筑物地震安全性提供理论依据。
1. 地下位移幅值的求解
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1.1 入射波场
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$ w_1^i({r_1}{\text{,}} {\theta _1}) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\varepsilon _m}} {({\text{-}}{\rm{i}})^m}{{\rm J}_m}(k{r_1})(\cos m\gamma \cos m{\theta _1} {\text{+}} \sin m\gamma \sin m{\theta _1}){\text{,}} $
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$ w_2^i({r_2}{\text{,}} {\theta _2}) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\varepsilon _m}} {({\text{-}} {\rm{i}})^m}{{\rm J}_m}(k{r_2})(\cos m\gamma \cos m{\theta _2} {\text{+}} \sin m\gamma \sin m{\theta _2}){\text{.}} $
(2) 1.2 散射波场
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$ w_1^r\left({{r_1}{\text{,}} {\theta _1}} \right) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(2 \right)}} \left({k{r_1}} \right)\left({{A_m}\cos m{\theta _1} {\text{+}} {B_m}\sin m{\theta _1}} \right){\text{,}} $
(3) $ w_2^r\left({{r_2}{\text{,}} {\theta _2}} \right) {\text{=}} \sum\limits_{n {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_n^{\left(2 \right)}} \left({k{r_2}} \right)\left({{A_n}\cos n{\theta _2} {\text{+}}{B_n}\sin n{\theta _2}} \right){\text{,}} $
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$ w_1^f\left({{r_1}{\text{,}} {\theta _1}} \right) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(2 \right)}} \left({{k_1}{r_1}} \right)\left({C_m^{(2)}\cos m{\theta _1} {\text{+}} D_m^{(2)}\sin m{\theta _1}} \right){\text{,}} $
(5) $ w_2^f\left({{r_1} {\text{,}} {\theta _1}} \right) {\text{=}}\sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(1 \right)}} \left({{k_1}{r_1}} \right)\left({C_m^{(1)}\cos m{\theta _1} {\text{+}} D_m^{(1)}\sin m{\theta _1}} \right) {\text{,}} $
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因此,在SH波入射的情形下,半空间中波的势函数为
$ {w^d} {\text{=}} w_1^i {\text{+}} w_2^i {\text{+}} w_1^r {\text{+}} w_2^r{\text{,}} $
(7) 衬砌介质中波的势函数为
$ {w^f} {\text{=}} w_1^f {\text{+}} w_2^f{\text{.}} $
(8) 1.3 引入边界条件求解问题
引入衬砌隧道的边界条件:
$ {\sigma _{rz}} {\text{=}} {\mu _1}\frac{{\partial {w^f}}}{{\partial {r_1}}} {\text{=}} 0 {\text{,}}{r_1} {\text{=}} b {\text{,}} $
(9) $ {w^d}{\text{=}} {w^f} {\text{,}} {r_1} {\text{=}} a {\text{,}} $
(10) $ {\mu _0}\frac{{\partial {w^d}}}{{\partial {r_1}}} {\text{=}} {\mu _1}\frac{{\partial {w^f}}}{{\partial {r_1}}}{\text{,}} {r_1}{\text{=}} a{\text{,}} $
(11) 即可求得式(3)—(6)中所有待定系数,从而确定式(7)中半空间介质内波的势函数。在SH波作用下,沿深度方向的位移可通过求解以上边界条件得出,从而求得隧道周围沿深度方向的位移幅值,具体求解过程不再赘述。
1.4 解的验证
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2. 算例与分析
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表 1 距地表6a深度范围内隧道左右两侧最大地下位移幅值Table 1. The maximum amplitude of underground displacement on both sides of tunnel within a depth of 6a from surface入射角/° 地下位移幅值 x/a=−3.0 x/a=−1.5 x/a=1.5 x/a=3.0 0 2.66 2.79 2.79 2.66 30 2.90 2.83 2.31 2.45 60 3.30 2.37 2.64 2.61 90 3.31 3.94 3.37 3.01 This page contains the following errors:
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3. 讨论与结论
本文应用波函数展开法和镜像法,得到了平面SH波作用下含圆形衬砌隧道的弹性半空间中散射波场的级数解答。通过数值算例分析,研究了平面SH的入射角度、入射频率和隧道埋深、衬砌刚度对沿线地下地震动的影响。结果表明,地下隧道的存在对其周围地震动具有显著的影响,并具有如下规律:
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2) 随着入射频率的增加,隧道周围土体的位移幅值有逐渐增大的趋势,但是在隧道左侧近处存在异常区,该区随着频率的增加地下位移振幅逐渐减小。
3) 在隧道周围近距离处,衬砌刚度的变化对地下位移幅值的影响显著,而在隧道远距离处,衬砌刚度对地下位移幅值的影响较弱。
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表 1 各地物的特征规则集
Table 1 The feature rule set of a variety of surface features
地物类别 特征规则集 阴影 亮度≤23 植被 均值≤30.5;矩形度≤0.9;紧致度≥1.61 完全损毁建筑物 GLCM角二阶矩≤0.002;GLCM同质性≤0.06;标准差≥30 基本完好建筑物 标准差≤12或标准差≥20;GLCM熵≥9;4.1≤GLDV熵≤4.3
1.925≤密度≤2.222或1.19≤密度≤1.39中度破坏建筑物 还未分类 注:GLCM为灰度共生矩阵,GLDV为灰度差分矢量。 表 2 矢量分类结果中建筑物数量
Table 2 The number of buildings achieved from the vector classification results
建筑物类型 实地调查所得数据个数 面向对象分类所得数据个数 基本完好建筑物 274 194 中度破坏建筑物 237 308 完全损毁建筑物 235 212 总计 746 714 表 3 各分类方法的精度评价
Table 3 Accuracy evaluation of various classification methods
方法 总体精度 Kappa系数 方法 总体精度 Kappa系数 SVM 70.37% 0.5966 基于CART算法决策树 76.22% 0.633 0 BP神经网络 74.96% 0.6537 面向对象 87.10% 0.819 3 表 4 各分类方法的错分率
Table 4 The error rate of various classification methods
方法 基本完好 中度破坏 完全损毁 SVM 36.73% 33.22% 18.62% BP神经网络 30.53% 30.24% 16.00% 基于CART算法决策树 37.00% 40.44% 9.08% 面向对象 20.00% 5.56% 14.29% -
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