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本文基于地震烈度数据点,用椭圆参数方程对烈度点空间分布进行最小二乘拟合,得到各烈度区椭圆烈度估计线,然后对烈度估计线进行统计回归,得到一组适用于青、甘、川、滇4省中强地震的椭圆烈度分布模型I=f(M,R).基于所建烈度分布模型,联立考虑中心点和方向性的椭圆数学方程,代入全部烈度数据点估算地震震级和宏观震中.本文的试算震例表明了此方法的可行性,并进行了不确定性分析.利用该方法对明清时期4次烈度点较少的中强历史地震参数进行了估算,所得结果表明了此方法对历史地震有效. 相似文献
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云南鲁甸6.5级地震灾害特点浅析 总被引:5,自引:4,他引:1
通过对2014年8月3日云南鲁甸6.5级地震震害开展实地调查,对灾区破坏情况进行总体介绍,并就各烈度区特征和建筑物震害、地震地质灾害、工程结构震害进行分析,初步得出本次地震的一些震害特点.一是灾区人口密度大,人员死亡较集中.人员死亡主要集中在Ⅷ和Ⅸ度区.二是地震振动强,灾区破坏严重.本次地震震源深度12km,极震区烈度高达Ⅸ度,震源破裂在11s内集中释放.三是抗震能力弱,房屋破坏严重.灾区属国家级贫困区,农村民居抗震能力弱,且多数民房坐落在河谷陡坡上,边坡效应加重房屋震害,重灾区砖木和土木房屋成片损毁、倒塌.四是灾区条件恶劣,救灾难度大.震区活动断裂密集发育、地质破碎疏松、地形崎岖不平,又恰值雨季,诱发极其严重次生地质灾害,导致人员伤亡,造成灾区大面积交通、通信、电力中断,救援物资与救援力量无法及时发挥作用. 相似文献
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The aim of this paper is to compute the ground-motion prediction equation (GMPE)-specific components of epistemic uncertainty, so that they may be better understood and the model standard deviation potentially reduced. The reduced estimate of the model standard deviation may also be more representative of the true aleatory uncertainty in the ground-motion predictions.The epistemic uncertainty due to input variable uncertainty and uncertainty in the estimation of the GMPE coefficients are examined. An enhanced methodology is presented that may be used to analyse their impacts on GMPEs and GMPE predictions. The impacts of accounting for the input variable uncertainty in GMPEs are demonstrated using example values from the literature and by applying the methodology to the GMPE for Arias Intensity. This uncertainty is found to have a significant effect on the estimated coefficients of the model and a small effect on the value of the model standard deviation.The impacts of uncertainty in the GMPE coefficients are demonstrated by quantifying the uncertainty in hazard maps. This paper provides a consistent approach to quantifying the epistemic uncertainty in hazard maps using Monte Carlo simulations and a logic tree framework. The ability to quantify this component of epistemic uncertainty offers significant enhancements over methods currently used in the creation of hazard maps as it is both theoretically consistent and can be used for any magnitude–distance scenario. 相似文献
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1956-2011年黑龙江省龙卷风气候特征 总被引:2,自引:0,他引:2
利用富士达—皮尔森强度分类法对1956—2011年黑龙江省229个龙卷风样本进行分类,分析龙卷风事件的时空分布特征,探讨典型龙卷风个例的环流背景及形成机制。结果表明:1956—2011年黑龙江省龙卷风灾害具有明显的时空分布特征,20世纪60—80年代龙卷风活动频繁,90年代龙卷风发生频次最少,2001—2011年龙卷风发生频次略增加。龙卷风主要集中发生在夏季,以7月发生最多,且多出现在午后至傍晚。对龙卷风空间分析发现黑龙江省绥化地区是龙卷风多发区,与该地区的地理位置、气候条件和大气环流特征有关。不稳定的形势场是龙卷风产生的基础,暖湿气流的输送和冷暖空气的强对流运动为龙卷风的产生提供了有利条件。 相似文献
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考虑震源破裂过程的青海玉树地震震动图研究 总被引:4,自引:0,他引:4
为了在缺少实时台站数据的情况下,研究震后地表地震动分布,本文尝试通过对震源做出某些约束,以提高震动图的精度.由于青海玉树地震的发震断层近似直立(倾向83°),震源破裂过程在时间和空间上呈明显的分区特征,分别考虑了地表破裂的线源发生模型和主震震级分解方法,用考虑了场地效应的震动图快速生成方法生成震动图.将两种方法生成的震动图与调查烈度比较.结果表明,基于震源破裂过程对主震震级进行分解,综合考虑各子事件生成震动图的方法,对震后的应急决策和灾情的快速评估是可行的. 相似文献
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Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes. 相似文献
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Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. 相似文献