震动图预测的不确定性及其应用
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摘要
破坏性地震常常导致大量人员伤亡和财产损失,给人类社会造成巨大灾难。目前,受多种条件所限,地震预报短期内很难取得突破性进展。为了减轻地震带来的灾害,必须加强抗震设防和地震应急研究。震动图作为地震应急的一项重要内容,能在地震发生后快速划定地震影响范围,是高效救灾的第一手资料。
震动图的产出涉及台站观测数据(强震、测震)、虚拟台站地震动参数估计、区域插值计算、震动烈度(仪器烈度)转换、场地效应校正等多个方面,各个环节均会造成不同程度的不确定性。鉴于此,USGS的ShakeMap(?)系统在产出仪器烈度分布、峰值加速度分布、峰值速度分布以及0.3s、1.0s、3.0s加速度反应谱分布的同时,还会发布不确定性分布图,以描述前四种图件的可信程度,并依据该图进行震害快速评估。国内震动图的研究还处于起步阶段,因而对其不确定性的研究还比较少。
在有强震台站的场点,可直接得到与结构破坏密切相关的加速度记录；在有测震台站的场点,可以利用测震台网的速度记录预测加速度,但此过程存在不确定性问题；在强震台站和测震台站均没有的场点,需要通过地面运动预测方程估计地震动参数,这就涉及地面运动预测方程的不确定性问题；在将地震动参数转换为震动烈度时,由于两者之间没有确定的对应关系,同样会涉及不确定性。论文从这三个方面着手,对影响震动图的不确定性问题就行研究,并将结果用于震害快速评估。另外,文章还构建了城镇地震重灾指数,以评定地震发生后特定城镇遭遇严重破坏的可能性。
本文主要工作如下：
1.利用美国NGA数据库的强震记录,拟合得到包含场地效应且以断层投影距为参数的新的地面运动预测方程。作者没有将其转换到中国大陆,原因在于：第一,本文选用的数据是全球板内活跃区浅层地壳的数据,并不是局限于一个地区；第二,由于烈度本身具有较高的不确定性,用烈度转换只会加大地面运动预测方程的不确定性；第三,新的预测方程采用断层投影距,而烈度预测方程多采用震中距,对大震而言震中距会造成较大误差。
2.从地面运动预测方程中的ε出发,提出了场点(城镇)遭遇不同烈度的概率计算方法：利用预测方程的估计值和预测方程的标准差,构造峰值加速度(PGA)变化的对数正态分布,以烈度分档对应的PGA范围,计算了震区各城镇遭遇不同烈度的概率及各城镇抗震设防烈度被超越的概率。
3.利用Hi-net和KiK-net共同记录到的282次Mj≥5.0、震源深度在100km以内的地震资料,分析了Hi-net速度微分与KiK-net加速度对数峰值比、对数反应谱比的分布特征及其随震级、距离的变化关系。同时,拟合得到Hi-net速度峰值与KiK-net加速度峰值、Hi-net速度微分峰值与KiK-net加速度峰值、Hi-net速度微分的反应谱与KiK-net加速度反应谱之间的对数线性关系。以上结论都表明,利用速度记录预测加速度不论是幅值还是频谱均存在一定的不确定性。
4.建立考虑不确定性的震动图模型,并利用此模型生成了庐山Ms7.0级地震的震动图,结果与USGS的ShakeMap符合良好。
5.将城镇烈度的概率计算方法用于庐山地震的震害快速评估。由于受选取的地震易损性模型所限,对经济损失的估计虽然与实际损失在同一数量级,但是偏低,而对死亡人数的估计则过高。
6.利用城镇烈度的概率计算方法,构建了城镇地震重灾指数。由于城镇重灾指数表达的是城镇遭遇严重破坏的可能性,所以可以为抗震救灾指挥部门快速制定紧急救援方案、调配救援力量提供重要参考。另外,华北地区设定地震下的城镇地震重灾指数,可以作为一个重灾数据库,为以后类似地震的应急救援提供必要的依据。
Destructive earthquakes often cause heavy casualties and property losses, posing a great disaster to human society. Because of the extreme complexity involved in the earthquake process, reliable earthquake prediction is not currently possible. In order to reduce earthquake damage, seismic fortification and earthquake emergency research must be strengthened. As a tool for earthquake response, shaking map can portray the extent of potentially damaging shaking following an earthquake, providing distinctly important information for earthquake relief.
The output of shaking map involves many aspects, such as observations (from macroseismograph or seismograph), ground motion prediction for phantom station, interpolation method, acquisition of shaking intensity (instrumental intensity), site effect and so on. Each of them can cause uncertainty in some ways. For this reason, the ShakeMap(?) system implemented by United States Geological Survey releases uncertainty map besides instrumental intensity, peak ground acceleration, peak ground velocity, and response spectrum. Since shaking map research in China is in its infancy, there is less study on its uncertainty.
If there is a macroseismograph, the ground motion acceleration, which is closely related to structure damage, can be obtained directly. If there is a seismograph, the ground motion acceleration can be estimated from the velocity record and there is uncertainty in the process. If there is no seismic station, the ground motion is just estimated from Ground Motion Prediction Equation (GMPE), which brings uncertainty due to GMPE itself. Lack of certain relationship between intensity and ground motion parameters, there is uncertainty when changing ground motion parameters into intensity. These uncertainties are analyzed in this dissertation and the results are used in rapid earthquake damage assessment in the end. Also, as another application of shaking map, the heavy damage index is built to judge which county will suffer heavy damage after an earthquake.
The main work and results are as follows:
1. A new Ground Motion Prediction Equation is gained based on NGA strong motion data, which includes site term and Joyner-Boore Distance. The new GMPE has not been converted for China Mainland and there are several reasons to do this:(1) NGA collects strong motion data from shallow crustal earthquake all over the world, not confined to one region.(2) Due to high uncertainty of intensity, the transform of GMPE from one region to another through intensity will result in increasing uncertainty.(3) The new GMPE adopts Joyner-Boore distance, while intensity prediction equation often uses epicentral distance.
2. We propose a method to compute the probability of shaking intensity for counties in seismic area by means of the stochastic variable ε in GMPE. Specifically, we build the logarithmic normal distribution about peak ground acceleration, using the estimated value and the standard deviation of the GMPE, to calculate the probability of every possible shaking intensity and the probability exceeding seismic fortification intensity for counties in seismic area. It is thought that the intensity displayed in a probability way is much more reasonable.
3. Using the data recorded by both Hi-net and KiK-net from282earthquakes with JMA magnitude greater than5and focal depth less than100km, we study the distribution of peak acceleration ratio and acceleration response spectrum ratio between Hi-net velocity differential and KiK-net observed acceleration, as well as the ratios in relation to magnitude and focal distance. Also, we obtained statistical relation in peak value between Hi-net velocity and KiK-net acceleration, as well as statistical relations in peak value and acceleration response spectrum between Hi-net velocity differential and KiK-net acceleration. It turns out that the differential of digital velocity record is different from observed acceleration in both amplitude and spectrum, which therefore cannot be directly used as acceleration.
4. We build a shaking map model which considers uncertainty. And it is employed to produce the shaking map for the Lushan earthquake, which conforms well to the result of USGS ShakeMap.
5. Using the method of computing the probability of shaking intensity for counties, we compute the probability of every possible shaking intensity for counties in Lushan area. The intensites and their probabilities are then used to estimate economic losses and casualties. Due to the vulnerability model, the economic losses, in spite of the same scale, are lower than the actual economic losses and the estimation of deaths is higher than the true number.
6. The heavy damage index, which is useful for earthquake emergency, is built based on the method of computing the probability of shaking intensity for counties to judge which county will suffer heavy damage after an earthquake. Besides, the heavy damage index for counties, under scenario earthquakes in North China, can be a damage database providing improtant information for future similar earthquakes.
震动图的产出涉及台站观测数据(强震、测震)、虚拟台站地震动参数估计、区域插值计算、震动烈度(仪器烈度)转换、场地效应校正等多个方面,各个环节均会造成不同程度的不确定性。鉴于此,USGS的ShakeMap(?)系统在产出仪器烈度分布、峰值加速度分布、峰值速度分布以及0.3s、1.0s、3.0s加速度反应谱分布的同时,还会发布不确定性分布图,以描述前四种图件的可信程度,并依据该图进行震害快速评估。国内震动图的研究还处于起步阶段,因而对其不确定性的研究还比较少。
在有强震台站的场点,可直接得到与结构破坏密切相关的加速度记录；在有测震台站的场点,可以利用测震台网的速度记录预测加速度,但此过程存在不确定性问题；在强震台站和测震台站均没有的场点,需要通过地面运动预测方程估计地震动参数,这就涉及地面运动预测方程的不确定性问题；在将地震动参数转换为震动烈度时,由于两者之间没有确定的对应关系,同样会涉及不确定性。论文从这三个方面着手,对影响震动图的不确定性问题就行研究,并将结果用于震害快速评估。另外,文章还构建了城镇地震重灾指数,以评定地震发生后特定城镇遭遇严重破坏的可能性。
本文主要工作如下：
1.利用美国NGA数据库的强震记录,拟合得到包含场地效应且以断层投影距为参数的新的地面运动预测方程。作者没有将其转换到中国大陆,原因在于：第一,本文选用的数据是全球板内活跃区浅层地壳的数据,并不是局限于一个地区；第二,由于烈度本身具有较高的不确定性,用烈度转换只会加大地面运动预测方程的不确定性；第三,新的预测方程采用断层投影距,而烈度预测方程多采用震中距,对大震而言震中距会造成较大误差。
2.从地面运动预测方程中的ε出发,提出了场点(城镇)遭遇不同烈度的概率计算方法：利用预测方程的估计值和预测方程的标准差,构造峰值加速度(PGA)变化的对数正态分布,以烈度分档对应的PGA范围,计算了震区各城镇遭遇不同烈度的概率及各城镇抗震设防烈度被超越的概率。
3.利用Hi-net和KiK-net共同记录到的282次Mj≥5.0、震源深度在100km以内的地震资料,分析了Hi-net速度微分与KiK-net加速度对数峰值比、对数反应谱比的分布特征及其随震级、距离的变化关系。同时,拟合得到Hi-net速度峰值与KiK-net加速度峰值、Hi-net速度微分峰值与KiK-net加速度峰值、Hi-net速度微分的反应谱与KiK-net加速度反应谱之间的对数线性关系。以上结论都表明,利用速度记录预测加速度不论是幅值还是频谱均存在一定的不确定性。
4.建立考虑不确定性的震动图模型,并利用此模型生成了庐山Ms7.0级地震的震动图,结果与USGS的ShakeMap符合良好。
5.将城镇烈度的概率计算方法用于庐山地震的震害快速评估。由于受选取的地震易损性模型所限,对经济损失的估计虽然与实际损失在同一数量级,但是偏低,而对死亡人数的估计则过高。
6.利用城镇烈度的概率计算方法,构建了城镇地震重灾指数。由于城镇重灾指数表达的是城镇遭遇严重破坏的可能性,所以可以为抗震救灾指挥部门快速制定紧急救援方案、调配救援力量提供重要参考。另外,华北地区设定地震下的城镇地震重灾指数,可以作为一个重灾数据库,为以后类似地震的应急救援提供必要的依据。
Destructive earthquakes often cause heavy casualties and property losses, posing a great disaster to human society. Because of the extreme complexity involved in the earthquake process, reliable earthquake prediction is not currently possible. In order to reduce earthquake damage, seismic fortification and earthquake emergency research must be strengthened. As a tool for earthquake response, shaking map can portray the extent of potentially damaging shaking following an earthquake, providing distinctly important information for earthquake relief.
The output of shaking map involves many aspects, such as observations (from macroseismograph or seismograph), ground motion prediction for phantom station, interpolation method, acquisition of shaking intensity (instrumental intensity), site effect and so on. Each of them can cause uncertainty in some ways. For this reason, the ShakeMap(?) system implemented by United States Geological Survey releases uncertainty map besides instrumental intensity, peak ground acceleration, peak ground velocity, and response spectrum. Since shaking map research in China is in its infancy, there is less study on its uncertainty.
If there is a macroseismograph, the ground motion acceleration, which is closely related to structure damage, can be obtained directly. If there is a seismograph, the ground motion acceleration can be estimated from the velocity record and there is uncertainty in the process. If there is no seismic station, the ground motion is just estimated from Ground Motion Prediction Equation (GMPE), which brings uncertainty due to GMPE itself. Lack of certain relationship between intensity and ground motion parameters, there is uncertainty when changing ground motion parameters into intensity. These uncertainties are analyzed in this dissertation and the results are used in rapid earthquake damage assessment in the end. Also, as another application of shaking map, the heavy damage index is built to judge which county will suffer heavy damage after an earthquake.
The main work and results are as follows:
1. A new Ground Motion Prediction Equation is gained based on NGA strong motion data, which includes site term and Joyner-Boore Distance. The new GMPE has not been converted for China Mainland and there are several reasons to do this:(1) NGA collects strong motion data from shallow crustal earthquake all over the world, not confined to one region.(2) Due to high uncertainty of intensity, the transform of GMPE from one region to another through intensity will result in increasing uncertainty.(3) The new GMPE adopts Joyner-Boore distance, while intensity prediction equation often uses epicentral distance.
2. We propose a method to compute the probability of shaking intensity for counties in seismic area by means of the stochastic variable ε in GMPE. Specifically, we build the logarithmic normal distribution about peak ground acceleration, using the estimated value and the standard deviation of the GMPE, to calculate the probability of every possible shaking intensity and the probability exceeding seismic fortification intensity for counties in seismic area. It is thought that the intensity displayed in a probability way is much more reasonable.
3. Using the data recorded by both Hi-net and KiK-net from282earthquakes with JMA magnitude greater than5and focal depth less than100km, we study the distribution of peak acceleration ratio and acceleration response spectrum ratio between Hi-net velocity differential and KiK-net observed acceleration, as well as the ratios in relation to magnitude and focal distance. Also, we obtained statistical relation in peak value between Hi-net velocity and KiK-net acceleration, as well as statistical relations in peak value and acceleration response spectrum between Hi-net velocity differential and KiK-net acceleration. It turns out that the differential of digital velocity record is different from observed acceleration in both amplitude and spectrum, which therefore cannot be directly used as acceleration.
4. We build a shaking map model which considers uncertainty. And it is employed to produce the shaking map for the Lushan earthquake, which conforms well to the result of USGS ShakeMap.
5. Using the method of computing the probability of shaking intensity for counties, we compute the probability of every possible shaking intensity for counties in Lushan area. The intensites and their probabilities are then used to estimate economic losses and casualties. Due to the vulnerability model, the economic losses, in spite of the same scale, are lower than the actual economic losses and the estimation of deaths is higher than the true number.
6. The heavy damage index, which is useful for earthquake emergency, is built based on the method of computing the probability of shaking intensity for counties to judge which county will suffer heavy damage after an earthquake. Besides, the heavy damage index for counties, under scenario earthquakes in North China, can be a damage database providing improtant information for future similar earthquakes.
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