国家地震台数据断记统计及批量补数自动处理

在VS2015开发环境下,使用C#语言编写国家地震台断记统计和批量补数自动处理程序,实现数据中断统计查询、数据采集器波形数据文件下载和上传波形数据文件至远程服务器的自动化。该自动处理程序在部分地震台使用以来,运行良好,数据完整率、连续率得到保障。     

基于XGBoost机器学习的地磁日变重构方法研究

为了重构或恢复存在严重干扰或数据缺失的台站观测数据,本文基于周边已有台站的高质量观测数据采用XGBoost机器学习方法重构地磁日变数据.仿真试验结果显示,无论是磁静日还是磁扰日,地磁场分量的绝对残差均值均低于0.1 nT.试验统计数据及重构结果残差曲线的对比分析表明,地磁日变重构精度与地磁活动性和待重构信号的时变剧烈程...     

时序形变资料的多核函数滤波方法研究及应用

针对多年时序形变观测资料有效信息提取复杂的问题,对基于多核函数的滤波方法进行研究,得到以下有益结论:(1)当核函数指数为0.5,光滑因子为0.003时,10天及以上核点间隔的滤波模型单位权中误差最小;(2)核点间隔控制滤波信息频谱的高低,间隔越大频谱信息越低,反之则频谱信息越高;(3)因数据缺失部分造成核点减少,当连续减少2个以上时滤波失败,当连续减少2个时数据缺失部分滤波出现失真,当减少1个时滤波效果不受影响;(4)通过对GPS时序资料、定点形变时序资料和非构造形变时序资料的滤波应用,获取不同频谱的信息,验证了本文方法的稳定性和可靠性。     

阿留申群岛地震台站方位角变化及其对横波分裂的影响

利用瑞雷波极化分析法计算了阿留申群岛上AK,AT,AV,IM,TA和XJ台网共59个地震台站的方位角及其随时间的变化。结果显示:阿留申群岛大部分台站的方位角与地理北极均存在一个小角度的偏差,极个别台站的偏差角度极大;部分台站的地震仪在摆放过程中受重新定北、安装新仪器等人为因素的影响,方位角大小在前后时段内并不一致。这充分说明台站方位角对横波分裂研究具有重要作用,当方位角偏离地理北极度数较大时,其对横波分裂研究的影响则不可被忽略,需要对地震数据分时段进行相应台站方位角的校正。      

地震地球物理观测台网地应变数据集

作为地壳形变监测的一种手段,地应变观测可监测微尺度下的地表应变变化,其分辨率一般为10-9。目前地震地球物理观测台网在网运行的洞体应变观测台站有111个,分量钻孔应变观测站59个,钻孔体应变测站有68个,产出数据包括原始观测数据、台站预处理数据和产品数据,可为地震研究、地球动力学研究提供重要的数据支撑。     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.     

云南GNSS时间序列共模分量提取分析

针对共模分量的精确获取问题,以2011—2018年云南31个GNSS连续站垂向时间序列为基础,选用区域叠加法和主成分分析法,提取得到共模分量的主要成分,对比分析了这两种方法的效果。结果表明:(1)由于云南垂向非线性运动空间一致性较好(测站间相关系数平均值为0.88),两种方法得到的共模分量较为一致;(2)两种方法提取得到的共模分量与全球水文负荷和大气负荷模型给出的位移时间序列接近(相关系数均为0.9),说明共模分量的主要成分为地表负荷变化引发的地壳垂向非构造运动;(3)共模分量不能用周期模型完全表示,还包含了年际间的运动差异等信息;(4)两种方法的空间滤波效果非常接近(WRMS减速比平均值都为0.70),测站的空间滤波效果与测站间相关系数呈显著的正相关。由于区域叠加法对测站数据完整率要求相对较低,因此建议当测站较少或者数据缺失较多时,采用区域叠加法;在测站较多且数据完整率较高时,建议采用主成分分析法。     

Homogeneity analysis of Turkish meteorological data set

The missing value interpolation and homogeneity analysis were performed on the meteorological data of Turkey. The data set has the observations of six variables: the maximum air temperature, the minimum air temperature, the mean air temperature, the total precipitation, the relative humidity and the local pressure of 232 stations for the period 1974–2002. The missing values on the monthly data set were estimated using two methods: the linear regression (LR) and the expectation maximization (EM) algorithm. Because of higher correlations between test and reference series, EM algorithm results were preferred. The homogeneity analysis was performed on the annual data using a relative test and four absolute homogeneity tests were used for the stations where non‐testable series were found due to the low correlation coefficients between the test and the reference series. A comparison was accomplished by the graphics where relative and absolute tests provided different outcomes. Absolute tests failed to detect the inhomogeneities in the precipitation series at the significance level 1%. Interestingly, most of the inhomogeneities detected on the temperature variables existed in the Aegean region of Turkey. It is considered that theseinhomogeneities were mostly caused by non‐natural effects such as relocation. Because of changes at topography at short distance in this region intensify non‐random characteristics of the temperature series when relocation occurs even in small distances. The marine effect, which causes artifical cooling effect due to sea breezes has important impact on temperature series and the orograhpy allows this impact go through the inner parts in this region. Copyright © 2010 John Wiley & Sons, Ltd.     

Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events

Abstract

This paper describes a fuzzy rule-based approach applied for reconstruction of missing precipitation events. The working rules are formulated from a set of past observations using an adaptive algorithm. A case study is carried out using the data from three precipitation stations in northern Italy. The study evaluates the performance of this approach compared with an artificial neural network and a traditional statistical approach. The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions. The problems that have yet to be addressed are overfitting and applicability outside the range of training data.     

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.     

基于样本增强的卷积神经网络震相拾取方法

为了快速、高效地从地震数据中识别地震事件和拾取震相,本文利用基于样本增强的卷积神经网络自动震相拾取方法,将西藏林芝地区L0230台站3个月数据作为训练集,该区内另外6个台站连续1个月的波形数据作为测试集,采用高斯噪声、随机噪声拼接、随机挑选噪声、随机截取地震事件等4种样本增强的方法扩增训练集,以提高自动震相拾取技术的准确率。结果显示:样本增强前模型在测试集上的地震事件识别准确率为80%,样本增强后提升至97%,表明样本增强有效地提高了模型的泛化性能和抗干扰能力;在0.5 s误差范围内,震相自动拾取准确率高于81%,在1.0 s误差范围内,准确率高于95%;利用基于样本增强的卷积神经网络震相拾取方法能够检测出人工拾取震相中误标和漏检的震相。      

Sensitivity of secular trends in precipitation data to observational errors

There has been considerable interest in estimating secular trends in precipitation data in various regions of the world. It is therefore important to ascertain the manner in which errors of observation affect estimated trends. For this purpose we have compared trends at 1219 stations in the contiguous United States for two data sets: (a) original observations, also called raw observations, and (b) the observations, adjusted to compensate for suspected errors. The adjustments were made at the National Climate Data Center, Asheville (Quinlan et al., 1987;karl andWilliams, 1987), In order to focus on the effects of observational errors we attempted to avoid the effects of filling of missing data by limiting the analysis to the period 1940–1984 for which the number of missing values is much smaller than earlier periods. A least-square linear regression was performed on the raw and adjusted data for each station and the slopes of the fitted lines were compared. The comparison was made for monthly, seasonal and annual precipitation values.The results for annual precipitation showed that 23 percent of the stations have trends of opposite signs in the raw and adjusted data. The trends were identical in annual data at only 11 percent of the stations. When monthly data are combined to form seasonal and annual averages the magnitude of the difference between the slopes of the adjusted and the raw observations generally increases, indicating that the errors in the individual monthly observations are correlated. When the station data were averaged to obtain state-wide averages, the effects of the errors became less pronounced in most of the states. These results indicate that obtaining trends in precipitation from station data is a more difficult problem than has been realized.     

Strong motion observations and recordings from the great Wenchuan Earthquake

The National Strong Motion Observation Network System （NSMONS） of China is briefly introduced in this paper. The NSMONS consists of permanent free-field stations, special observation arrays, mobile observatories and a network management system. During the Wenchuan Earthquake, over 1,400 components of acceleration records were obtained from 460 permanent free-field stations and three arrays for topographical effect and structural response observation in the network system from the main shock, and over 20,000 components of acceleration records from strong aftershocks occurred before August 1, 2008 were also obtained by permanent free-field stations of the NSMONS and 59 mobile instruments quickly deployed after the main shock. The strong motion recordings from the main shock and strong aftershocks are summarized in this paper. In the ground motion recordings, there are over 560 components with peak ground acceleration （PGA） over 10 Gal, the largest being 957.7 Gal. The largest PGA recorded during the aftershock exceeds 300 Gal.     

Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain,Iran

ABSTRACT

Groundwater-level time series often have a substantial number of missing values which should be taken into consideration before using them for further analysis, particularly for numerical groundwater flow modelling applications. This study aims to comprehensively compare two data-driven models, singular spectrum analysis (SSA) and multichannel spectrum analysis (MSSA), to reconstruct groundwater-level time series and impute the missing values for 25 piezometric stations in Ardabil Plain, northwest Iran. The reconstructed groundwater-level time series are assessed against the complete observed groundwater time series, while the imputed values are appraised against the artificially created gap values. The results show that both SSA and MSSA demonstrate a solid competency in imputation and reconstruction of groundwater-level data. However, depending on the spatial correlation between the piezometers, and the most suitable probability distribution function (pdf) fitted to the time series of each piezometer, the performance may vary from piezometer to piezometer.     

华北地区地磁加卸载响应比方法应用研究

运用地磁加卸载响应比方法(LURR)对华北地区23个地磁台站2008—2014年的数字化地磁相对记录数据进行计算分析,结果显示华北地区7年内发生了38次共计10组加卸载响应比异常,其中7组有震例对应,在选定的24次震例中16次有震前异常。经统计发现各次异常发生时其异常面积与最大异常强度成正比关系,各组异常内累计异常台站数量与组内地震辐射能量有一定相关性。对有震例的7组异常分析后认为多数情况下组内各次异常之间有一定联系,主要表现为位置的连续性及继承性,并且发现震中位置和异常位置分布有一定关系。     

Stochastic evaluation of simple pairing approaches to reconstruct incomplete rainfall time series

Two-station pairing approaches are routinely used to infill missing information in incomplete rainfall databases. We evaluated the performance of three simple methodologies to reconstruct incomplete time series in presence of variable nonlinear correlation between data pairs. Nonlinearity stems from the statistics describing the marginal peak-over-threshold (POT) values of rainfall events. A Monte Carlo analysis was developed to quantitatively assess expected errors from the use of chronological pairing (CP) with linear and nonlinear regression and frequency pairing (FP). CP is based on a priori selection of regression functions, while FP is based on matching the probability of non-exceedance of an event from one time series with the probability of non-exceedance of a similar event from another time series. We adopted a generalized Pareto (GP) model to describe POT events, and a t-copula algorithm to generate reference nonlinearly correlated pairs of random temporal distributions distributed according with the GP model. The results suggest that the optimal methodology strongly depends on GP statistics. In general, CP seems to provide the lowest errors when GP statistics were similar and correlation became linear; we found that a power-2 function performs well for the selected statistics when the number of missing points is limited. FP outperforms the other methods when POT statistics are different and variables are markedly nonlinearly correlated. Ensemble-based results seem to be supported by the analysis of observed precipitation at two real-world gauge stations.     

广东省台网震级ML的台站校正值

利用广东省地震台网多年的观测资料,选取了481个地震的数据计算了19个台站的M_L震级校正值。初步的结果表明,震级校正值受震中距、震级等因素影响较小,可以满足实际工作要求。     

Assessment of Primary Production by Statistical Analysis of Water‐quality Data

Time series of weekly water‐quality data at Schnackenburg on the Elbe River (1985—2000) were subjected to principal component analysis (PCA). Considering the amplitudes of composite patterns of variables is a step towards a process‐oriented interpretation of waterquality data. One specific objective was to investigate the impact of improved water quality after the German reunification in 1990 on primary production and the oxygen budget. To discriminate anthropogenic signals from natural fluctuations a separation of the impact of discharge was attempted based on a linear regression approach. A dominant pattern of co‐variation in the residual data could be attributed to biological activity (primary production). The most relevant variables of this 'biomode' are oxygen saturation, pH, and orthophosphate. We conclude that multivariate statistical analysis of water‐quality data can help to estimate primary production when direct observations of algal concentrations are missing. In the years from 1998—2000 the trend of the ‘biomode’ indicates an increased load of oxygen consuming biomass caused by enhanced primary production in the middle stretches of the Elbe River which corresponds with the observation of more severe oxygen deficits in the tidal section of the river.     

美国中东部地震台阵波场的三分量波形梯度

波形梯度法是一种全新的台阵数据处理技术,该方法利用子台阵中的波形差异可以得到一些基本的地震波传播参数.本文首次将其应用于美国中东部地震台阵面波的三分量研究当中.首先通过垂向分量的面波进行波形梯度分析,得到垂直分量面波的相速度、传播方向、几何扩散和辐射花样.再通过垂向分量得到的传播方向进行坐标旋转,从而得到了径向分量以及切向分量的面波,再将其应用于波形梯度分析,分别得到径向分量以及切向分量面波的相速度、传播方向、几何扩散和辐射花样.利用2012年8月27日发生在中美洲西海岸地震事件的三分量地震数据的面波波形得到的结果显示,在相同周期,研究区域的三个分量面波的相速度分布横向差异显著.切向分量面波的相速度分布特征差异较大,可能是由于径向各向异性造成的.三个分量的传播方向变化都不大,且切向分量的传播方向变化大于垂直分量与径向分量,说明地震波的切线分量在传播过程中受到的影响更大,同时还可以看出传播方向的变化呈现出条带状的特征.几何扩散和辐射花样都是与地震波的振幅项有关的信息,三个分量的几何扩散特征基本一致.但是由于切向分量传播方向变化相对较大,可能导致了切向分量面波的辐射花样有所差异.     

Spatial and temporal characteristics of droughts in the western part of Bangladesh

Spatial and temporal characteristics of droughts in the western part of Bangladesh have been analysed. Standardized precipitation index method is used to compute the severity of droughts from the rainfall data recorded in 12 rainfall gauge stations for the period of 1961–1999. An artificial neural network is used to estimate missing rainfall data. Geographic Information System (GIS) is used to map the spatial extent of droughts of different severities in multiple time scales. Critical analysis of rainfall is also carried to find the minimum monsoon and dry months rainfall require in different parts of the study area to avoid rainfall deficit. The study shows that the north and north‐western parts of Bangladesh are most vulnerable to droughts. A significant negative relationship between multiple ENSO index and rainfall is observed in some stations. Analysis of seasonal rainfall distribution, rainfall reliability and long‐term rainfall trend is also conducted to aid prediction of future droughts in the area. Copyright © 2007 John Wiley & Sons, Ltd.