基于行星际/太阳风和地磁条件的紫外极光卵边界建模和预测

极光卵的尺度大小与太阳风-磁层-电离层能量耦合过程紧密相关,准确预测其大小对空间天气研究和预报具有非常重要的意义.本文基于模糊c均值聚类算法,从Polar卫星紫外极光图像中自动提取极光卵边界数据(~1215000个赤道向边界点和~3805000极向边界点),统计分析其与太阳风等离子体、行星际磁场、地磁指数等之间的相关特性,并构建了以行星际、太阳风为模型参数(模型1)和以行星际、太阳风及地磁指数为模型参数(模型2)的2种极光卵边界多元回归模型.以模型预测的极光卵边界与实际极光卵边界之间的平均绝对误差作为模型评价标准,将本文预测模型与Carbary(2005)模型和Milan(2009)模型进行了对比.结果表明,模型2对极光卵极向、赤道向边界预测的平均绝对误差为1.55和1.66地磁纬度,优于Carbary和Milan模型(Carbary模型极向、赤道向边界的平均绝对误差为2.18和5.47地磁纬度,Milan模型极向、赤道向边界的平均绝对误差为1.71地磁纬度和1.90地磁纬度).     

星载远紫外极光/气辉探测发展综述

地球热层和电离层是近地卫星运行的主要场所,也是空间天气对人类活动影响的重要区域.气辉和极光是高层大气中的重要发光现象,其辐射特性、特别是紫外波段的辐射特性,是高层大气和电离层物理化学过程信息的重要来源、本文详细分析了国外远紫外极光/气辉探测和相应探测器的发展里程,并结合我国相关研制技术和水平,提出了发展星载极光/气辉探测的基本考虑和建议.     

地磁活动指数的过去、现在和未来

地磁活动性是范围广、分量多、种类繁、形态异、频谱宽、原因复杂的时变过程.面对如此复杂的地磁活动性,除了分门别类逐一进行深入研究之外,根据实测资料,用简便易行的分类和简单明了的指标,对地磁活动性进行总体检阅和形态学描述也是非常必要的.于是,各种地磁活动指数应运而生.地磁指数不仅是表征地磁活动现象的重要参数,其应用范围逐渐扩展到日地能量耦合计算、电离层一磁层一太阳风空问环境状态描述、空间环境监测、空间天气预报等许多相关领域."地磁活动指数化"的思想正在向别的领域扩展,其构建原理和量算方法也在不断改进和完善.本文将现有的约50种地磁活动指数分两大类型,综述其提出背景和发展过程,分析其使用范围和优缺点,探讨地磁活动指数未来发展的趋势.     

太阳风暴的日冕行星际过程三维数值研究进展

如何能够提前几个小时到几天的时间预报空间天气不仅是空间天气研究的重要课题,而且也是现代科技发展对空间天气预报的重大需求.基于物理的以强大计算能力为基础的太阳风暴日冕行星际过程三维数值研究已经成为灾害性空间天气预报建模的重要手段,并在近几年取得了很大的发展.本文聚焦于背景太阳风状态和太阳扰动事件在行星际空间传播的三维数值...     

基于遗传算法的广义回归神经网络在地震预测中的应用

提出了利用基于遗传算法结合广义回归神经网络（GRNN）进行地震预测的新方法。利用遗传算法的全局搜索能力、不易陷入局部极小点等优点来优化GRNN的径向基函数中心、宽度以及输出层的权值,使得计算结果全局最优。在实际地震数据中选取了100组样本数据进行仿真实验,并利用MATLAB进行仿真。仿真结果表明,本文提出的方法具有较高的精度和一定的理论指导意义。     

基于神经网络和滤波技术的贮仓结构参数识别

利用神经网络和Kalman滤波技术,提出了一种直接识别结构物理参数的方法,用Kalman滤波技术训练网络。在贮仓振动台实验的基础上,用贮仓在动载作用下的位移、速度作为网络的输入,激振加速度和响应加速度作为网络的输出。仿真计算表明,本文方法是可行的。     

基于AlexNet卷积神经网络模型的宁夏天然地震和非天然地震识别研究

宁夏回族自治区及周边天然地震和非天然地震频发,精确快速识别天然和非天然地震有利于震后应急响应、科学研究、赈灾救援等工作。基于AlexNet卷积神经网络模型,选取宁夏境内及周边130个地震事件（天然地震80个、非天然地震50个）,对其进行了单个台站波形记录地震事件的训练和多个台站波形记录地震事件的测试,并将模型结果与宁夏测震台网人工编目结果进行比对,结果表明单个台站波形记录地震事件的训练结果中,AlexNet卷积神经网络模型对宁夏天然地震和非天然地震的正确识别率为99%;多个台站波形记录地震事件的测试结果中,此模型对宁夏天然地震和非天然地震的正确识别率为97.01%。AlexNet卷积神经网络模型作为人工智能领域的成熟技术之一,可以很好的运用于宁夏天然地震和非天然地震的识别工作之中。     

甘肃及邻区基于地震预警参数τc和Pd的震级快速计算模型研究

地震预警作为目前能够有效减轻地震灾害的手段之一,可以在破坏性地震动来临之前提供几秒至几十秒的预警时间,提醒预警区域采取紧急处置措施以减轻人员伤亡和财产损失。地震预警是一个复杂的系统,其中利用地震参数快速、准确地进行实时震级估算是其有效发挥减灾作用的关键。文章基于2012—2020年发生在甘肃省及邻区的190个地震共821条记录(3.1≤M≤6.6),计算分析在P波1~10 s时间窗长度内,两种地震预警参数(卓越周期τ_c和位移幅值P_d)分别在垂直向、水平向和三分向均值测项三种不同条件下与峰值参数PGV、PGD的相关性,拟合相应的预警震级快速计算模型,并将估算震级M_E与实际震级M_C进行比较。结果表明：时间窗长度的改变对结果有一定的影响,而测项改变造成的差别较小。此外,在拟合度、预测残差均值和标准差,以及残差分布等预测效果方面,采用参数P_d的模型性能优于参数τ_c。考虑到参数和震级的实时计算以及预警信息发布的时效性要求,推荐在该地区应用垂直向记录P波到时3 s后的参数P_d来进行地震预警震级实时快速计算。     

On the prediction of relativistic electron fluence based on its relationship with geomagnetic activity over a solar cycle

A study has been carried out to determine the relationship between high energy relativistic (>2 MeV) electron fluence and auroral zone geomagnetic activity for a solar cycle. Data for 1987–1997, spanning Solar Cycle 22, were used in the study. The relativistic electron fluence data were based on fluxes observed by the GOES geosynchronous satellites. The geomagnetic data were the DRX indices derived from a Canadian magnetic observatory located in the auroral zone at Fort Churchill, near the footprint of field lines passing through geostationary satellites. This work, based on data from a solar cycle, confirms earlier findings using limited data from segments of a solar cycle of enhancement in fluence 2–3 days after increases in geomagnetic activity, and shows the cycle dependence of fluence with respect to geomagnetic activity. This study underlines the influence of recurrent coronal holes on fluence level as well as the possible role of Pc5 magnetic pulsations as an electron acceleration mechanism, and highlights the predictability of fluence from ground geomagnetic data. A fluence prediction algorithm can now solely be based on derived expressions relating fluence and DRX. Thus, a simple fluence prediction scheme can easily be implemented to provide a 2–3 day advance warning of space weather conditions hazardous to geosynchronous satellites, since during days of high fluence, the likelihood of internal charging in a satellite is high, with possible discharges that could result in satellite operational anomalies. For verification purpose, daily values of fluence for 1997–2000 and for January 1994 were postcast using the derived expressions. The postcast values were validated, and the results give credence to the fluence prediction scheme.     

Modeling the Effects of Meteorological Factors on SO2 and PM10 Concentrations with Statistical Approaches

Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO₂ and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO₂ levels. In regression models calculated R² values were 0.89 and 0.75 for PM10 and SO₂, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R² values were obtained as 0.89 and 0.69 for PM10 and SO₂, respectively, by using appropriate networks.     

基于小波神经网络耦合模型对云南星云湖富营养化气象驱动因子的分析

气象因子是影响湖泊富营养化的重要因素,而湖泊富营养化对人群健康、生态系统和社会经济等均有负面影响.本文基于统计资料及遥感数据,结合Morlet小波分析和BP多层前馈神经网络(BP神经网络)构建了不同时间尺度下的小波神经网络耦合模型,分析了19862011年云南星云湖水华强度变化与月降雨量、月平均气温、月平均风速、月日照...