一种改进的近断层脉冲型地震动模拟方法

本文基于小波包技术的随机地震动模拟方法,提出一种改进的参数化随机近断层脉冲型地震动模拟方法。然后,通过识别和提取近断层脉冲型地震动数据库中脉冲型地震动的特征参数,建立了基于震源、传播路径和场地特征等参数的脉冲模型参数预测方程。最后,通过模拟实际记录和误差分析检验了改进的模拟方法的有效性。结果表明:应用改进的模拟方法得到的地震动时程无论在波形、频率特性还是峰值上均与实际记录具有较好的一致性。改进的模拟方法在保留地震动时频非平稳性的基础上,能够有效地提高近断层脉冲型地震动的模拟效果,并且能够很好地体现脉冲型地震动的主要特征。     

On the Compensation of Systematic Errors in Marine Gravity Measurements

Based on an analysis of the source of errors in marine gravity measurements, an error model, firstly, is constructed mathematically which can characterize the change of systematic errors and with which a new crossover adjustment model is presented in this paper. Then, two methods of compensating the systematic errors are proposed, i.e., the self-calibrating adjustment and the a-posteriori compensation. Some questions involved in solving the adjustment problem, such as the rank deficiency, the choice of error model, the weighting of model parameters and the significance test of compensation efficiency, etc., are discussed in detail. Finally, a practical survey network is used as a case study to test the efficiency and reliability of the two compensation methods.     

扫描式F-P干涉仪在MERINO观测中的热层风结果与分析

MERINO是沿120°E/60°W子午圈的全球联网观测项目,热层中性风是其中一个十分重要的观测参数.本文介绍了武汉大学扫描式F-P干涉仪(SFPI)及其在MERINO第一轮观测中的结果,并与同站点的流星雷达在98 km高度上的测风数据进行了验证,分析表明两台设备的吻合性很好,SFPI风速测量的误差分别为8 m·s^-1(557.7 nm)和7.8 m·s^-1 (630.0 nm).本文对地基扫描式FPI的误差做了详细分析,结果表明在FPI仪器设计之初,就需要考虑标准具通光孔径D和成像物镜焦距f的合理组合,在目前CCD和标准具规格的限制下,增加f会提高测风精度,但会牺牲通光量,可通过设置CCD为Binning读出模式来提高灵敏度.在FPI进行风场观测时,要对设备工作的环境温度进行控制,特别是控制标准具的温度漂移.FPI的测风精度可以理论推导,但实际测风误差还与反演算法、元器件的加工和安装精度有关,国际通用的误差评价方法是用统计偏差来表示.     

自动气象站雨量误差分析及维护

在分析翻斗式雨量传感器产生误差的现象与原因的基础上,介绍了具体的调整和维护方法,并认为雨量调整涉及的问题较多,也比较复杂,只有在确定是由于传感器基点位置不正确而造成的雨量误差时,才做基点调整,且一般不提倡台站自行进行调整.     

The demographics of energy and mobility poverty: Assessing equity and justice in Ireland,Mexico, and the United Arab Emirates

Energy and mobility poverty limits people’s choices and opportunities and negatively impinges upon structural economic and social welfare patterns. It also hampers the ability of planners to implement more equitable and just decarbonization pathways. Research has revealed that climate policies have imposed a financial burden on low-income and other vulnerable groups by increasing food and energy prices, leading as well to global inequality. Similarly, researchers have warned that in developing countries, emission mitigation policies could increase poverty rates and even frustrate progress towards universal access to clean energy. This research explores whether low-income social groups experience a 'double energy vulnerability', a situation that simultaneously positions people at heightened risk of transport and energy poverty. We investigate this 'double vulnerability' through original data collection via three nationally representative surveys of Mexico (N = 1,205), the United Arab Emirates (N = 1,141), Ireland and Northern Ireland (N = 1,860). We draw from this original data to elaborate on the sociodemographic attributes, expenditure and behaviour emerging from energy and transport use, focusing on themes such as equity, behaviour and vulnerability. We propose energy and transport poverty indexes that allow us to summarize the key contributing factors to energy and transport poverty in the countries studied and uncover a strong correlation between these two salient forms of poverty. Our results suggest that energy and transport poverty are common issues regardless of the very different national, and even sub-national, contexts. We conclude that energy and transport poverty requires target policy interventions suitable for all segments of society, thus enabling contextually-tailored, just energy transitions.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.