汶川地震前远场形变异常及其与地震相关性研究     

牛安福  张凌空  章静  闫伟  赵静  岳冲  苑争一 《中国地震》2021,37(1):15-21

判定震前出现的形变显著异常是否与地震相关,是认识地震孕育过程及开展地震预测研究最为重要的基础问题.本文分析了汶川地震前一些显著形变异常特征,发现震前在震中区附近异常较少,而外围则较多.针对其中一些距离震中较远的异常,借助前兆异常与地震震中距、震级间物理统计模型,提出了远距离形变异常与地震关联的判别准则,给出了红柳峡跨断...  相似文献   

南疆西部沙尘天气长期变化及突变特征分析          下载免费PDF全文

玛依热.艾海提  周元逸  赵海军  阿依夏木·买买提托合提  胡素琴 《沙漠与绿洲气象(新疆气象)》2023,17(3):107-115

利用南疆西部15个国家气象站1961—2019年逐日沙尘天气资料，采用气候倾向率和统计检验等方法对南疆西部沙尘天气的时空变化特征进行分析。研究表明：春季为南疆西部沙尘暴及浮尘天气出现最多的季节、扬沙天气出现次多的季节，分别占全年沙尘暴、扬沙、浮尘的49%、38%、43%；夏季为扬沙天气出现最多的季节、是沙尘暴、浮尘天气出现次多的季节，分别占全年沙尘暴、扬沙、浮尘的35%、43%、35%；冬季为低频季节，发生占比分别为7%、6%、14%。南疆西部沙尘天气呈东多西少特征，山区沙尘天气日数明显少于平原，浮尘天气平原地区分布均匀，沙尘暴、扬沙平原东部和南部区域多于平原腹地。沙尘天气日数年际变化振幅较大，沙尘暴、扬沙、浮尘日数整体呈明显减少趋势。浮尘年际变化周期显著，其次为扬沙与沙尘暴，1984和1977年为沙尘暴、浮尘统计定义上的突变年份，扬沙存在2个突变点，分别为1982和1992年。沙尘暴和扬沙的主导风向为偏西北风，浮尘主导风向为偏东北风，主导风向与地形影响关联密切。  相似文献   

Multi-hazard susceptibility mapping based on Convolutional Neural Networks     

《地学前缘(英文版)》2022,13(5):101425

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.  相似文献