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971.
972.
973.
基于北京市测震台网连续三分量地震计波形数据,计算28个测震台站台基噪声,利用Welch方法计算噪声功率谱密度(PSD),进而计算地震台台基1-20 Hz地动噪声均方根值(RMS)和观测动态范围。结果表明,依照《地震台站观测环境技术要求》,北京市测震台网中有11个Ⅰ类台、9个Ⅱ类台、6个Ⅲ类台、2个Ⅳ类台。通过分析北京市测震台网数字地震台背景噪声水平,为测震台网的规划建设提供数据支持。 相似文献
974.
瓦屋山水库地震台网于2016年恢复重建,恢复重建的主要任务是对4个台站的观测设施进行改造、台站设备的升级换代、数据传输方式的恢复等,4个台站台址和1个台网中心不变。经过1年的恢复重建,瓦屋山水库地震台网考核运行率达到99.31%,监测能力满足可监测ML≥0.5地震的要求,2个台站的观测动态范围优于恢复重建前的水平,2个台站不如恢复重建前。 相似文献
975.
本文提出了一种基于深度学习卷积神经网络(CNN)的全波形反演方法,可对地震散射波场中的散射体进行成像和定位.本文的灵感来自如下猜想:在散射波场剖面上的每个点附近的局部波场与该点到各散射体之间的最小距离有关系,并且这个关系可以被CNN网络所识别.我们将该最小距离定义为散射距离场,并将散射距离场的类别(即大小等级)作为CNN网络的预期输出,而输入就是该点附近的局部波场.最后用上述CNN网络对散射波场进行逐点训练和识别.计算结果证实了我们的灵感猜想,即上述CNN网络能够在复杂散射波场中对散射体进行成像.只通过一个训练模型的学习,CNN网络即可反演多种散射模型的偏移剖面,最后得到"类别函数预测值"和"滤波剖面"两种成像结果,由此可以辨识出在复杂的偏移剖面中各散射体的位置. 相似文献
976.
瓦屋山水库地震台网于2016年恢复重建,恢复重建的主要任务是对4个台站的观测设施进行改造、台站设备的升级换代、数据传输方式的恢复等,4个台站台址和1个台网中心不变.经过1年的恢复重建,瓦屋山水库地震台网考核运行率达到99.31%,监测能力满足可监测ML≥0.5地震的要求,2个台站的观测动态范围优于恢复重建前的水平,2个台站不如恢复重建前. 相似文献
977.
动态域名服务是一种把固定域名解析为动态IP的网络新技术。简要地介绍了动态域名服务技术,并以广东省地震信息网的动态域名服务系统为例,介绍了如何在Windows 2000 Server下实现动态域名服务。 相似文献
978.
Synchronous patterns of variation in NO3 concentration, observed in upland lakes and streams across gradients of climate, altitude, land use and nitrogen deposition over the last decade, show a strong negative correlation with winter values for the North Atlantic Oscillation Index and mean winter temperature. Variations in a longer term stream NO3 record validate this relationship, and although mechanisms are yet unclear, may be linked to the length of time the soil profile remains frozen during the winter. These findings are of particular significance to our understanding of the relationship between atmospheric N deposition and concentrations and fluxes of NO3 in runoff, and the potential response to future climatic changes. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
979.
Global sustainability governance is marked by a highly fragmented system of distinct clusters of international organizations, along with states and other actors. Enhancing inter-organizational coordination and cooperation is thus often recognized as an important reform challenge in global sustainability governance. The 17 Sustainable Development Goals, agreed by the United Nations in 2015, thus explicitly aim at advancing policy coherence and institutional integration among the myriad international institutions. Yet, have these goals been effective in this regard? We assess here the impact of the Sustainable Development Goals on the network structure of 276 international organizations in the period 2012–2019, that is, four years before and four years after the launch of the Sustainable Development Goals. The network structure was approximated by analyzing data from the websites of these 276 international organizations that were joined by more than 1.5 million hyperlinks, which we collected using a custom-made web crawler. Our findings are contrary to what is widely expected from the Sustainable Development Goals: we find that fragmentation has in fact increased after the Sustainable Development Goals came into effect. In addition, silos are increasing around the 17 SDGs as well as around the social, economic, and environmental dimensions of sustainable development. 相似文献
980.
《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