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1.
We propose a novel method that combines gray system theory and robust M-estimation method to suppress the interference in controlled-source electromagnetic data. We estimate the standard deviation of the data using a gray model because of the weak dependence of the gray system on data distribution and size. We combine the proposed and threshold method to identify and eliminate outliers. Robust M-estimation is applied to suppress the effect of the outliers and improve the accuracy. We treat the M-estimators of the preserved data as the true data. We use our method to reject the outliers in simulated signals containing noise to verify the feasibility of our proposed method. The processed values are observed to be approximate to the expected values with high accuracy. The maximum relative error is 3.6676%, whereas the minimum is 0.0251%. In processing field data, we observe that the proposed method eliminates outliers, minimizes the root-mean-square error, and improves the reliability of controlled-source electromagnetic data in follow-up processing and interpretation.  相似文献   

2.
电磁类地球物理方法由于极易受到各类噪声的干扰,使得估计的视电阻率曲线或相位曲线发生畸变,严重地影响了反演解译精度,如何对这类观测曲线进行合理有效地平滑,是目前数据处理中的重点,也是难点.本文把各向异性扩散（anisotropic diffusion AD）引入到曲线平滑中,提出了各向异性扩散的曲线平滑方法,以平滑点梯度值的降函数作为扩散速度,在梯度较大的位置予以较小的扩散速度以保护特征,在梯度较小的位置予以较大的扩散速度平滑噪声引起的扰动.同时为了减小“飞点”(outliers)对平滑的干扰,采用局部单调扩散进行预处理.实验结果显示,本文提出的平滑方法有效地平滑了噪声与“飞点”的干扰,恢复了曲线的基本形态,有效地保持了曲线基本特征.  相似文献   

3.
A number of deblending methods and workflows have been reported in the past decades to eliminate the source interference noise recorded during a simultaneous shooting acquisition. It is common that denoising algorithms focusing on optimizing coherency and weighting down/ignoring outliers can be considered as deblending tools. Such algorithms are not only enforcing coherency but also handling outliers either explicitly or implicitly. In this paper, we present a novel approach based on detecting amplitude outliers and its application on deblending based on a local outlier factor that assigns an outlier-ness (i.e. a degree of being an outlier) to each sample of the data. A local outlier factor algorithm quantifies outlier-ness for an object in a data set based on the degree of isolation compared with its locally neighbouring objects. Assuming that the seismic pre-stack data acquired by simultaneous shooting are composed of a set of non-outliers and outliers, the local outlier factor algorithm evaluates the outlier-ness of each object. Therefore, we can separate the data set into blending noise (i.e. outlier) and signal (i.e. non-outlier) components. By applying a proper threshold, objects having high local outlier factors are labelled as outlier/blending noise, and the corresponding data sample could be replaced by zero or a statistically adequate value. Beginning with an explanation of parameter definitions and properties of local outlier factor, we investigate the feasibility of a local outlier factor application on seismic deblending by analysing the parameters of local outlier factor and suggesting specific deblending strategies. Field data examples recorded during simultaneous shooting acquisition show that the local outlier factor algorithm combined with a thresholding can detect and attenuate blending noise. Although the local outlier factor application on deblending shows a few shortcomings, it is consequently noted that the local outlier factor application in this paper obviously achieves benefits in terms of detecting and attenuating blending noise and paves the way for further geophysical applications.  相似文献   

4.
In optical dating, especially single-grain dating, various patterns of distributions in equivalent dose (De) are usually observed and analysed using different statistical models. None of these methods, however, is designed to deal with outliers that do not form part of the population of grains associated with the event of interest (the ‘target population’), despite outliers being commonly present in single-grain De distributions. In this paper, we present a Bayesian method for detecting De outliers and making allowance for them when estimating the De value of the target population. We test this so-called Bayesian outlier model (BOM) using data sets obtained for individual grains of quartz from sediments deposited in a variety of settings, and in simulations. We find that the BOM is suitable for single-grain De distributions containing outliers that, for a variety of reasons, do not form part of the target population. For example, De outliers may be associated with grains that have undesirable luminescence properties (e.g., thermal instability, high rates of anomalous fading) or with contaminant grains incorporated into a sample when collected in the field or prepared in the laboratory. Grains that have much larger or smaller De values than the target population, due to factors such as insufficient bleaching, beta-dose heterogeneity or post-depositional disturbance, may also be identified as outliers using the BOM, enabling these values to be weighted appropriately for final De and age determination.  相似文献   

5.
This paper proposes methods to detect outliers in functional data sets and the task of identifying atypical curves is carried out using the recently proposed kernelized functional spatial depth (KFSD). KFSD is a local depth that can be used to order the curves of a sample from the most to the least central, and since outliers are usually among the least central curves, we present a probabilistic result which allows to select a threshold value for KFSD such that curves with depth values lower than the threshold are detected as outliers. Based on this result, we propose three new outlier detection procedures. The results of a simulation study show that our proposals generally outperform a battery of competitors. We apply our procedures to a real data set consisting in daily curves of emission levels of nitrogen oxides (NO\(_{x}\)) since it is of interest to identify abnormal NO\(_{x}\) levels to take necessary environmental political actions.  相似文献   

6.
Amino acid racemization (AAR) is a cost-effective method for dating the large numbers of specimens required for time-averaging studies. Because the aim of time-averaging studies is to determine the structure of the age distribution, any data screening must be done cautiously and systematically. Methods to quantitatively assess the quality of AAR data and to identify aberrant specimens are under-developed. Here we examine a variety of screening criteria for identifying outliers and determining the suitability of specimens for numerical dating including: high serine concentrations (modern contamination), covariance of aspartic acid (Asp) and glutamic acid (Glu) concentrations (diagenetic influences), replication of measurements (specimen heterogeneity), and the relation between Asp and Glu d/l values (internal consistency). This study is based on AAR analyses of 481 late Holocene shells of four molluscan taxa (Ethalia, Natica, Tellina, and Turbo) collected from shallow sediment cores from the central Great Barrier Reef. Different outliers are flagged by the different screening criteria, and 6% of specimens were found to be unsuitable for time-averaging analyses based on screening the raw AAR data. We recommend a hybrid approach for identifying outliers and specimens for numerical dating.  相似文献   

7.
Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.  相似文献   

8.
Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

9.
Understanding radioxenon time series and being able to distinguish anthropogenic from nuclear explosion signals are fundamental issues for the technical verification of the Comprehensive Nuclear-Test-Ban Treaty. Every radioxenon event categorisation methodology must take into account the background at each monitoring site to uncover anomalies that may be related to nuclear explosions. Feedback induced by local meteorological patterns on the equipment and on the sampling procedures has been included in the analysis to improve a possible event categorisation scheme. The occurrence probability of radioxenon outliers has been estimated with a time series approach characterising and avoiding the influence of local meteorological patterns. A power spectrum estimator for radioxenon and meteorological time series was selected; the randomness of the radioxenon residual time series has been tested for white noise by Kolmogorov–Smirnov and Ljung–Box tests. This methodological approach was applied to radioxenon data collected at two monitoring sites located at St. John’s, Canada and Charlottesville, USA, equipped with two different noble gas systems. It shows different feedback with local meteorological patterns and randomness for the radioxenon data recorded at the selected sites of St. John’s and Charlottesville as well as a different occurrence probability of the outliers in the normalized radioxenon original and residual time series.  相似文献   

10.
Station corrections for body wave travel times are required to compensate for lateral variations in the crust and uppermost mantle in the analysis of seismic travel times that are used to determine deep Earth structure by various methods, including tomography. Station corrections to be applied to P wave arrival times from teleseismic earthquakes recorded by the Kaapvaal seismic network were estimated by five different methods: (1) averaging, (2) computing the median, and (3) weighted averaging of residuals; (4) least-squares regression, and (5) weighted least-squares regression. The corrections display variations that are related to the tectonic features of southern Africa inferred from surface geology, clearly delineating the southern and central areas of both the Kaapvaal and Zimbabwe cratons as regions of early arrivals, and the area around the Bushveld complex by later arrivals. Use of a simple ray method for generating synthetic station corrections suggests that lateral variations in the top 230 km of the Earth can explain the observed pattern of variations in station corrections. A satisfactory way of compensating for the biasing effects of outliers in the individual estimates of station corrections is through adaptation of a method originally developed by Jeffreys, which involves ascribing weights to the observations that reduce the standard deviation on a single estimate of a station correction from 0.123 to 0.096 s. Methods (2), (3) and (5) avoid serious bias by outliers, although methods (3) and (5) are preferred, because they also provide information on the causes of outliers. The presence of some outliers cannot be explained by errors in the measurement process, but must be caused by timing errors at the stations during recording, and/or errors introduced during the process of constructing the archived data files from the field data.  相似文献   

11.
采用小地震的丛集性活动资料定量地研究地下断层的三维几何信息对地震危险性评估具有至关重要的意义。若所使用的资料存在高比率的离群值,将会使断层几何参数的估值产生较大偏差。为了提高断层几何参数估值的稳健性,本文将随机抽样一致性（RANSAC）与网格搜索（GS）相结合,给出了随机抽样一致性-网格搜索（RANSAC-GS）估算方法。在数值模拟试验中,对模拟观测值加入1%,5%,10%和20%的离群值,分别采用GS和RANSAC-GS两种方法估算了断层面倾角,并从反演参数的残差、反演模型计算值与观测值的密合度、目标函数及相关度等方面证明了RANSAC-GS方法即使在高比率离群值的情况下,依然能够给出稳健的参数估值。最后,利用2008年1月至2012年12月鄂尔多斯地区小震重定位结果,以震源点到断层面距离最小为准则,采用RANSAC-GS方法反演获得太谷断裂的断层面倾角为52.5°,与前人的结果有较好的一致性,在此基础上对大地测量形变观测给出合理的阐释,从而证明了本文方法的有效性。   相似文献   

12.
Here we present a new algorithm (StalAge), which is designed to construct speleothem age models. The algorithm uses U-series ages and their corresponding age uncertainty for modelling and also includes stratigraphic information in order to further constrain and improve the age model. StalAge is applicable to problematic datasets that include outliers, age inversions, hiatuses and large changes in growth rate. Manual selection of potentially inaccurate ages prior to application is not required. StalAge can be applied by the general, non-expert user and has no adjustable free parameters. This offers the highest degree of reproducibility and comparability of speleothem records from different studies. StalAge consists of three major steps. Firstly, major outliers are identified. Secondly, age data are screened for minor outliers and age inversions, and the uncertainty of potential outliers is increased using an iterative procedure. Finally, the age model and corresponding 95%-confidence limits are calculated by a Monte-Carlo simulation fitting ensembles of straight lines to sub-sets of the age data.We apply StalAge to a synthetic stalagmite ’sample’ including several problematic features in order to test its performance and robustness. The true age is mostly within the 95%-confidence age limits of StalAge showing that the calculated age models are accurate even for very difficult samples. We also apply StalAge to three published speleothem datasets. One of those is annually laminated, and the lamina counting chronology agrees with the age model calculated by StalAge. For the other two speleothems the resulting age models are similar to the published age models, which are both based on smoothing splines. Calculated uncertainties are in the range of those calculated by combined application of Bayesian chronological ordering and a spline, showing that StalAge is efficient in using stratigraphic information in order to reduce age model uncertainty.The algorithm is written in the open source statistical software R and available from the authors or as an electronic supplement of this paper.  相似文献   

13.
Expectation Maximization algorithm and its minimal detectable outliers   总被引：1，自引：0，他引：1  
Minimal Detectable Biases (MDBs) or Minimal Detectable Outliers for the Expectation Maximization (EM) algorithm based on the variance-inflation and the mean-shift model are determined for an example. A Monte Carlo method is applied with no outlier and with one, two and three randomly chosen outliers. The outliers introduced are recovered and the corresponding MDBs are almost independent from the number of outliers. The results are compared to the MDB derived earlier by the author. This MDB approximately agrees with the MDB for one outlier of the EM algorithm. The MDBs for two and three outliers are considerably larger than MDBs of the EM algorithm.  相似文献   

14.
ABSTRACT

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions.  相似文献   

15.
抗差自适应Kalman滤波算法中,抗差等价权矩阵和自适应因子的计算,要求观测信息具有多余观测量且准确可靠,但在动态变形监测应用中,通常滤波观测值仅为三维坐标且存在较强噪声和粗差的影响。为此,先对该算法中的自适应因子和抗差等价权矩阵的计算进行研究和改进,然后计算了某高速公路边坡的GPS动态监测数据。结果表明,抗差自适应Kalman滤波能够有效地抵制动态变形监测中观测值异常的影响。  相似文献   

16.
Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and to estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959–2010) for the Garonne river in the southwest of France. Results show that the accuracy of forecasts is improved in the robust model with respect to the unrobust model for the quarter-monthly flows. By reducing the number of parameters to be estimated, the principle of parsimony favors the choice of the robust approach.  相似文献   

17.
The robustness of large quantile estimates of largest elements in a small sample by the methods of moments (MOM), L‐moments (LMM) and maximum likelihood (MLM) was evaluated and compared. Bias (B) and mean square error (MSE) were used to measure the estimation methods performance. Quantiles were estimated by eight two‐parameter probability distributions with the variation coefficient being the shape parameter. The effect of dropping largest elements of the series on large quantile values was assessed for various variation coefficient (CV)/sample size (n) ‘combinations’ with n = 30 as the basic value. To that end, both the Monte Carlo sampling experiments and an asymptotic approach consisting in distribution truncation were applied. In general, both sampling and asymptotic approaches point to MLM as the most robust method of the three considered, with respect to bias of large quantiles. Comparing the performance of two other methods, the MOM estimates were found to be more robust for small and moderate hydrological samples drawn from distributions with zero lower‐bound than were the LMM estimates. Extending the evaluation to outliers, it was shown that all the above findings remain valid. However, using the MSE variation as a measure of performance, the LMM was found to be the best for most distribution/variation coefficient combinations, whereas MOM was found to be the worst. Moreover, removal of the largest sample element need not result in a loss of estimation efficiency. The gain in accuracy is observed for the heavy‐tailed and log‐normal distributions, being particularly distinctive for LMM. In practice, while dealing with a single sample deprived of its largest element, one should choose the estimation method giving the lowest MSE of large quantiles. For n = 30 and several distribution/variation coefficient combinations, the MLM outperformed the two other methods in this respect and its supremacy grew with sample size, while MOM was usually the worst. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

18.
 It is well known that the computation of higher order statistics, like skewness and kurtosis, (which we call C-moments) is very dependent on sample size and is highly susceptible to the presence of outliers. To obviate these difficulties, Hosking (1990) has introduced related statistics called L-moments. We have investigated the relationship of these two measures in a number of different ways. Firstly, we show that probability density functions (pdf ) that are estimated from L-moments are superior estimates to those obtained using C-moments and the principle of maximum entropy. C-moments computed from these pdf's are not however, contrary to what one may have expected, better estimates than those estimated from sample statistics. L-moment derived distributions for field data examples appear to be more consistent sample to sample than pdf 's determined by conventional means. Our observations and conclusions have a significant impact on the use of the conventional maximum entropy procedure which typically uses C-moments from actual data sets to infer probabilities.  相似文献   

19.
When gravimetric data observations have outliers, using standard least squares (LS) estimation will likely give poor accuracies and unreliable parameter estimates. One of the typical approaches to overcome this problem consists of using the robust estimation techniques. In this paper, we modified the robust estimator of Gervini and Yohai (2002) called REWLSE (Robust and Efficient Weighted Least Squares Estimator), which combines simultaneously high statistical efficiency and high breakdown point by replacing the weight function by a new weight function. This method allows reducing the outlier impacts and makes more use of the information provided by the data. In order to adapt this technique to the relative gravity data, weights are computed using the empirical distribution of the residuals obtained initially by the LTS (Least Trimmed Squares) estimator and by minimizing the mean distances relatively to the LS-estimator without outliers. The robustness of the initial estimator is maintained by adapted cut-off values as suggested by the REWLSE method which allows also a reasonable statistical efficiency. Hereafter we give the advantage and the pertinence of REWLSE procedure on real and semi-simulated gravity data by comparing it with conventional LS and other robust approaches like M- and MM-estimators.  相似文献   

20.
将形变速率累加法应用于山西地区4个定点水准观测资料分析,研究计算结果与本区域及邻区中、强地震的对应关系,发现震前一些连续的异常“孤立”点,经过积分放大后,异常信息的提取更直观、突出,显示出较好的映震效果;该方法对于跨断层资料的异常识别,具有一定的参考意义。  相似文献   

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