共查询到18条相似文献，搜索用时 437 毫秒
1.
本次研究围绕张衡一号(ZH-1)卫星监测到的2021年青海玛多MS7.4地震前的电离层效应,采用上下四分位距法和小波变换法处理ZH-1卫星朗缪尔探针(LAP)探测到的电子密度Ne和等离子体分析仪(PAP)探测到的离子密度Ni(He+和O+)数据,经分析发现Ne、 He+密度和O+密度在震前7天(2021年5月14日)和5天(2021年5月16日)出现同步异常。在此基础上,基于地球物理异常多项证据,分析认为2021年玛多MS7.4地震孕育过程包括应力扩张早期、应力扩张晚期和临震加速期,该地震可能通过岩石圈—大气圈—电离层耦合模型的声重波、电磁波通道影响了电离层。 相似文献
2.
采用张衡一号电磁卫星朗缪尔探针(Langmuir Probe, LAP)载荷原位电子密度对夏季磁静日时段新疆区域(30°～55°N, 70°～100°E)的顶部电离层Ne进行分析。通过分析日侧和夜侧的数据变化曲线：40°～55°N范围内Ne数据变化平稳，日侧Ne值平均高于夜侧Ne值的2倍，其中夜侧Ne值稳定在0.5×1010 cm-3～1.0×1010 cm-3,日侧Ne稳定在1.0×1010 cm-3～1.5×1010 cm-3;而30°～40°N范围上Ne数据波动较大。根据地方时特征分析结果表明：无论是夜侧还是日侧，随着纬度的降低Ne值逐渐增大，Ne值最大达到5.25×1010 cm... 相似文献
3.
利用2007—2013年电离层测高仪位于磁赤道观测站Jicamarca (11.95°S,76.8°W,地磁纬度为1°N)的垂测数据和COSMIC掩星反演电离层资料,分析了不同太阳活动条件下两种探测技术获取电离层特征参数峰值密度NmF2和峰值高度HmF2的相关性,同时也探讨了国际参考电离层模型IRI输出参数与测高仪垂测数据的相关性.此外,进一步分析了COSMIC掩星和IRI模型在不同地方时高估或低估垂测参数的分布特征.结果表明:(1)由COSMIC掩星反演和IRI模型输出参数NmF2与测高仪垂测值NmF2得到的相关系数都在0.8以上.太阳活动低年COSMIC探测得到的NmF2相关性高于太阳活动高年得到的结果,但IRI模型在太阳高年得到的NmF2相关性好于太阳活动低年的计算结果.(2) 由COSMIC掩星反演和IRI模型输出的HmF2与测高仪垂测值HmF2在春秋季得到的相关性较高,夏季的相关性最弱.由COSMIC掩星探测HmF2得到的季节和时间相关系数大都集中在0.8和0.6以上,但由IRI模型得到的HmF2相关性降低,特别是太阳活动低年的夏季和傍晚其相关系数低于0.3.(3)太阳低年COSMIC掩星和IRI模型白天时段大都高估、夜间至凌晨前后低估电离层参数NmF2和HmF2;但太阳活动高年NmF2在地方时午夜后则呈现高估的特点.相关结果为未来IRI模型的进一步完善以及低纬地区电离层同化模式研究提供参考. 相似文献
4.
提出一种基于Facebook 开源的Prophet预测模型进行电离层TEC异常识别的新方法。 首先, 对比分析了该方法与传统时间序列预测方法(ARIMA模型等)预测电离层TEC建模背景值的精度, 以及与经典电离层TEC异常识别方法(滑动四分位法)提取前面对应一致的电离层TEC背景值的精度。 结果表明, Prophet预测模型预测建模背景值的精度要明显优于其他方法, 且预测的建模精度比ARIMA模型等方法高2.55倍左右, 比滑动四分位法高10.74倍左右。 同时, 在最佳预测建模区间时, 其精度值大小比较依次为RMSEIQR=10.5841>RMSEARIMA=3.2780>RMSEProphet=0.8469, 说明传统探测法预测建模背景值时具有较大的不足。 随后, 以2017年8月8日九寨沟7.0级地震为例, 利用该方法分析了电离层TEC异常扰动情况, 并对比验证了该方法的有效性和准确性。 实验结果表明: 在震前第10 d和第2 d电离层TEC发生较为明显的负异常, 第7 d电离层TEC发生较为明显的正异常。 对比实验表明, Prophet预测模型的有效性和准确性明显优于滑动四分位法。 相似文献
5.
2019年6月17日四川长宁发生MS6.0地震, 之后发生了一系列的强余震, 为更好地分析此次地震的序列特征以及强余震的可预测属性, 采用国际上对复杂序列拟合相对较好的“传染型余震序列”(ETAS)模型以及基于Reseanberg-Jones(R-J)模型发展的Omi-R-J模型, 通过连续滑动、 拟合和余震发生率预测, 对地震序列的模型参数稳定性、 预测结果进行了比较研究, 并利用N-test、 T-test检验方法对预测结果进行了效能评估。 结果表明, 相比于其他中强震序列参数, 此次长宁MS6.0地震序列参数中反映激发能力的αETAS较其他序列明显偏小, 而反映衰减能力的pORJ值和应力累积水平的bORJ值相对较小, 与此次余震序列丰富、 持续时间相对较长相吻合; ETAS和Omi-R-J模型对于复杂序列在[3.0, 3.5, 4.0]三个震级档的强余震仍具有一定的预测能力; 总体的“每个地震的信息增益”(IGPE)计算结果显示, ETAS模型略优于Omi-R-J模型, 前者或更适合复杂地震序列的余震预测。 相似文献
6.
基于龙门山断裂带2012年1月—2021年9月MS2.5及以上地震目录数据,按震级分组建立发震时刻间隔序列,然后对各序列进行平稳白噪声检验,自相关、偏相关性分析,使用SARIMA模型对其进行短、中、长周期拟合及预测。通过分析模型拟合效果,得到不同震级序列的最优模型参数及相应周期数值。其中,序列MS≥2.5及序列MS≥3.0各模型调整R2均在0.86以上,最高达0.911;两序列对应模型的短时预测表现良好,预测RMSE分别为10.686及8.800。模型预测结果表明,龙门山断裂带后续发震时刻间隔总体趋势平稳,序列MS≥3.0预测结果趋势有微弱增长,一段时间内龙门山断裂带MS≥3.0地震发震次数将略微下降,地震活动性降低。该分析结果可为地震活动研究工作提供科学依据,其分析方法及过程为地震发震时间的分析预测提供了有效可行的途径。 相似文献
7.
利用三流辐射传输模型研究了极区高太阳天顶角和不同直射辐射占比下水下漫射衰减系数的剖面变化.本文首先利用2009年9～10月波弗特海18个辐射观测数据验证了三流辐射模型在极区的适用性.统计显示,在无海冰影响下,辐射传输模型获得的490nm下行辐照度(Ed(490))和漫射衰减系数(Kd(490))与观测值的平均相对误差分别为7.04%和9.88%.而在海冰包围的站点,由于冰遮挡造成辐射观测数据偏小,模型模拟的平均相对误差达到15.89%和15.55%.其次,在不同叶绿素浓度与不同直射辐射占比环境下的模拟显示,在表层30m以浅,高太阳天顶角对Kd(490)影响较大. 30m以深Kd(490)受光场(包括太阳天顶角和直射辐射占比)影响较小,与固有光学量(吸收系数与后向散射系数)满足线性关系.对比发现表层Kd(490)在高太阳天顶角下与50m以深的水体一致,意味着高太阳天顶角下(大于60°),表层漫射衰减系数亦可认为是固有光学参数.计算发现在叶绿素浓度大于0.05mg m-3... 相似文献
8.
利用漠河站、左岭站、富克站垂测仪数据和COSMIC反演的电离层资料,分析比较了太阳活动高年两种探测手段获取的电离层特征参量(NmF2、hmF2)的相关性.结果表明,两种方式获取的电离层对应特征参量相关性较高,且NmF2的相关性好于hmF2,同时相关性与纬度和季节有关.在地磁中纬度地区对应参量相关性较好,而在地磁低纬度受北驼峰控制区域相关性降低;在电离层赤道异常区域,春秋季、夏季对应特征参量相关性好于冬季.造成冬季相关性低的可能原因是,在跨越赤道中性风作用下,冬季电离层赤道异常区电子浓度梯度较大,造成掩星反演误差增大,致使两种探测手段获取的电离层特征参量相关性降低. 相似文献
9.
大气加权平均温度Tm是GNSS探测大气可降水量PWV(Precipitable Water Vapor)的关键参数．目前，加权平均温度模型主要包括线性模型和非线性模型．本文基于2011—2015年期间的编号54511北京探空测站的有效探测资料，建立Tm与Ts的线性和非线性(一阶傅里叶函数、一元二次函数)关系；利用2016年探空站实测资料对所建模型及常用模型进行对比分析，从RMSE、Bias及波动范围评价参数发现Tm＿G模型精度高于常用模型，而再分析资料ERA-Interim建立的加权平温度Tm＿ERA模型和新非线性Tm模型精度相差甚小，且误差概率分布趋近于正态分布；因此，新建模型能有效避免了通用Bevis全球模型在特定区域导致的区域性精度偏差问题，尤其在探空站缺乏的区域，可以采用ERA-Interim产品建立Tm模型．通过对不同Tm模型获取IGS站BJFS的PWV结果与相应时间54511探空站的实测PWV数据进行... 相似文献
10.
大地电磁测深资料数据采集过程中,由于温度、湿度等对仪器的影响或GPS搜星不正常,采集到的数据有时会出现时间序列跳帧或缺失现象.针对这一问题,本文将基于无激励AR(p)模型预测数据的原理引入大地电磁测深数据处理中.根据已知序列确定AR(p)模型阶数以及模型参数,建立正确的预测模型对缺失数据进行预测,并对比经过预测后的数据与实际样本数据的频谱,表明AR(p)预测模型可以解决原始资料的不连续性问题,提高了大地电磁测深野外资料的利用率. 相似文献
11.
Based on a combination of a radial basis function network (RBFN) and a self‐organizing map (SOM), a time‐series forecasting model is proposed. Traditionally, the positioning of the radial basis centres is a crucial problem for the RBFN. In the proposed model, an SOM is used to construct the two‐dimensional feature map from which the number of clusters (i.e. the number of hidden units in the RBFN) can be figured out directly by eye, and then the radial basis centres can be determined easily. The proposed model is examined using simulated time series data. The results demonstrate that the proposed RBFN is more competent in modelling and forecasting time series than an autoregressive integrated moving average (ARIMA) model. Finally, the proposed model is applied to actual groundwater head data. It is found that the proposed model can forecast more precisely than the ARIMA model. For time series forecasting, the proposed model is recommended as an alternative to the existing method, because it has a simple structure and can produce reasonable forecasts. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
12.
本文介绍如何利用GRACE两颗卫星之间K波段双频微波精密测距和轨道数据,得到星间平均电子密度.发展了一种将连续轨道电子密度极小对齐到零的方法,以消除整周模糊度;借助CHAMP卫星朗缪探针测量得到的轨道电子密度基值以及GPS掩星数据计算的等离子体垂直梯度标高,进一步修正了GRACE星间电子密度所固有的偏差;从而得到大约500 km高度上长达近十年的全球电子密度数据.为了检验消除偏差后GRACE星间电子密度数据的可靠性,对比了GRACE卫星过Millstone Hill雷达上空时,非相干散射雷达观测到的大致同时和相近位置的电子密度数据,结果显示,二者之间的线性相关系数为0.97,平均偏差为-7.26%,GRACE星间电子密度总体稍微偏低,偏差的标准差为18.6%.为进一步验证本文方法所得数据的可用价值,利用消除偏差后的电子密度数据,对GRACE卫星与CHAMP卫星在近乎相同的地方时而高度不同的近圆极轨道上飞行的情况下,两颗卫星观测到的电子密度随经度和纬度的全球分布进行了对比分析.多方面的对比检验证明,本文方法得到的几乎连续10年的GRACE高度上全球电子密度数据基本可靠,为电离层气候学与天气学研究提供了宝贵资料. 相似文献
13.
Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input
Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi-arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross-correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance. 相似文献
14.
Abstract Time series modelling approaches are useful tools for simulating and forecasting hydrological variables and their change through time. Although linear time series models are common in hydrology, the nonlinear time series model, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, has rarely been used in hydrology and water resources engineering. The GARCH model considers the conditional variance remaining in the residuals of the linear time series models, such as an ARMA or an ARIMA model. In the present study, the advantages of a GARCH model against a linear ARIMA model are investigated using three classes of the GARCH approach, namely Power GARCH, Threshold GARCH and Exponential GARCH models. A daily streamflow time series of the Matapedia River, Quebec, Canada, is selected for this study. It is shown that the ARIMA (13,1,4) model is adequate for modelling streamflow time series of Matapedia River, but the Engle test shows the existence of heteroscedasticity in the residuals of the ARIMA model. Therefore, an ARIMA (13,1,4)-GARCH (3,1) error model is fitted to the data. The residuals of this model are examined for the existence of heteroscedasticity. The Engle test indicates that the GARCH model has considerably reduced the heteroscedasticity of the residuals. However, the Exponential GARCH model seems to completely remove the heteroscedasticity from the residuals. The multi-criteria evaluation for model performance also proves that the Exponential GARCH model is the best model among ARIMA and GARCH models. Therefore, the application of a GARCH model is strongly suggested for hydrological time series modelling as the conditional variance of the residuals of the linear models can be removed and the efficiency of the model will be improved. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Modarres, R. and Ouarda, T.B.M.J., 2013. Modelling heteroscedasticty of streamflow times series. Hydrological Sciences Journal, 58 (1), 1–11. 相似文献
15.
Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey 总被引：6，自引：3，他引：3
Ömer Faruk Durdu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1145-1162
In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is
addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models
known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average
(SARIMA) to predict drought in the Büyük Menderes river basin using SPI as drought index. Temporal characteristics of droughts
based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged
periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management.
ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking.
In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results
of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and
Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated
model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected
ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the
model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted
data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable
results up to 2 months ahead. The stochastic models developed for the Büyük Menderes river basin can be employed to predict
droughts up to 2 months of lead time with reasonably accuracy. 相似文献
16.
针对降雨输入不确定性对实时洪水预报影响的问题,本文采用不考虑未来预报降雨、考虑未来预报降雨、考虑预报降雨的降雨量误差和降雨时间误差4种方法,以陕西省两个半湿润流域（陈河流域和大河坝流域）为研究区域,分析不同预见期和不同降雨输入情况下洪水预报的精度.研究表明：相对于不考虑未来降雨情况,考虑未来降雨后在预报预见期较长时对预报结果精度提升较大,在预见期较短时对预报结果精度提升不显著;暴雨中心位置不同对预报精度影响也不同,当暴雨中心位于流域下游时降雨量误差对流量预报误差影响更大;降雨量误差主要影响洪量相对误差和洪峰相对误差,且这种影响是线性的,对确定性系数的影响是非线性的二次函数,降雨时间误差主要影响峰现时间误差. 相似文献
17.
Forecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools is powerful in temporal data analysis. Classical geostatistical methods provide the best estimates of spatial data. In the present work a hybrid framework for space–time groundwater level forecasting is proposed by combining a soft computing tool and a geostatistical model. Three time series forecasting models: artificial neural network, least square support vector machine and genetic programming (GP), are individually combined with the geostatistical ordinary kriging model. The experimental variogram thus obtained fits a linear combination of a nugget effect model and a power model. The efficacy of the space–time models was decided on both visual interpretation (spatial maps) and calculated error statistics. It was found that the GP–kriging space–time model gave the most satisfactory results in terms of average absolute relative error, root mean square error, normalized mean bias error and normalized root mean square error. 相似文献
18.
ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) 总被引：1，自引：0，他引：1
Ujjwal Kumar V. K. Jain 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):751-760
In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has
been adapted to forecast daily mean ambient air pollutants (O3, CO, NO and NO2) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied
to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions,
namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion),
and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function)
inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q)
models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean
absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts,
one step (i.e., one day) ahead MAPE for CO, NO2, NO and O3, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in
the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach
is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning
purposes. 相似文献