共查询到20条相似文献，搜索用时 15 毫秒
1.
全球范围内大量布设的GNSS(Global Navigation Satellite System)参考网为精密定位、导航和授时等应用提供了丰富的数据资源.基于局域参考网,先后发展了若干侧重实现双频精密定位的技术,如NRTK(Network Real Time Kinematic),PPP(Precise Point Positioning)和PPP-RTK等.其中,PPP-RTK融合了NRTK和PPP的技术优势,是目前相关研究的热点.本文改进了利用局域参考网提取各类改正信息的算法,以便于实现单频PPP-RTK,具体步骤包括:1)逐参考站实施非组合PPP,并固定已知站星距和卫星钟差,预估电离层延迟、浮点模糊度等参数;2)联合所有参考站的PPP模糊度预估值,通过重新参数化,形成一组双差整周模糊度和接收机、卫星相位偏差;3)固定双差整周模糊度,精化求解卫星相位偏差和各参考站PPP电离层延迟.基于网解中用到的卫星轨道和钟差,以及网解所提供的卫星相位偏差和(内插的)电离层延迟,参考网内的单频流动站即可实施PPP-RTK.基于澳大利亚某连续运行参考站网和流动站的实测数据,考察了:1)参考网数据处理中,双差模糊度的固定成功率(98.89%)和卫星相位偏差估值的时间稳定性(各连续弧段优于0.2周);2)流动站处电离层延迟的内插精度(优于10cm);3)单天内任一历元起算,固定静态(动态)单频PPP整周模糊度所需时长(均不超过10min);4)模糊度固定前后,单频动态PPP的定位精度(模糊度固定后,平面和天顶RMS分别优于5cm和10cm;模糊度固定前,相应RMS仅为28~53cm). 相似文献
2.
地磁参量实时测量野值的在线辨识与改正是影响地磁导航算法定位概率和精度的重要因素.地磁导航定位的研究目前主要集中于匹配定位算法,地磁参量实时测量数据处理的研究较少.本文将多层递阶非平稳时间序列预测模型引入海洋地磁参量实时测量野值在线辨识与改正,利用多层递阶模型对实时测量值进行一步预测,根据迪克松准则进行野值辨识,利用中值滤波和一步预测值对野值进行改正.仿真数据和实测数据的实验室仿真结果表明,本文提出的野值在线辨识与改正算法不仅可以检测全部孤立型野值而且对多数斑点型野值也有较好的辨识效果,对产生野值处信号的复原误差小于5%. 相似文献
3.
Flood season segmentation, which partitions an entire flood season into multiple subseasons, constitutes a considerable water resources management task. Moreover, the risks associated with various schemes for flood season segmentation should be evaluated. Preliminary analysis in this study used the principal component based outlier detection (PCOut) algorithm to identify possible outlying observations to reduce the uncertainty involved in flood season segmentation. Then, a quantitative measurement, the seasonal exceedance probability (SEP), was proposed to evaluate various segmentation schemes. The SEP quantifies the risk that the maximum observation occurs outside the main flood season. Several findings were derived based on a case study of China’s Three Gorges Reservoir (TGR) and daily streamflow records (1882–2010). (1) The PCOut algorithm was found effective in identifying outliers, and the estimation uncertainty of the segmentation evaluation due to outliers decreased when the end date of main flood season (EDMFS) was postponed. (2) The proposed SEP measurement was shown capable of supporting quantitative evaluation of the segmentation schemes in the flood season. (3) The current flood segmentation scheme based on an EDMFS of September 10 is sufficiently safe for the TGR. The findings of this study could help in the proper operation of the TGR. 相似文献
4.
Peter Schürch Alexander L. Densmore Nicholas J. Rosser Michael Lim Brian W. McArdell 《地球表面变化过程与地形》2011,36(14):1847-1859
Detection of surface change is a fundamental task in geomorphology. Terrestrial laser scanners are increasingly used for monitoring surface change resulting from a variety of geomorphic processes, as they allow the rapid generation of high‐resolution digital elevation models. Irrespective of instrument specifics, survey design or data processing, such data are subject to a finite level of ambiguity in position measurement, a consideration of which must be taken into account when deriving change. The propagation of errors is crucial in change detection because even very small uncertainties in elevation can produce large uncertainties in volume when extrapolated over an area of interest. In this study we propose a methodology to detect surface change and to quantify the resultant volumetric errors in areas of complex topography such as channels, where data from multiple scan stations must be combined. We find that a commonly proposed source of error – laser point elongation at low incidence angles – has a negligible effect on the quality of the final registered point cloud. Instead, ambiguities in elevation inherent to registered datasets have a strong effect on our ability to detect and measure surface change. Similarly, we find that changes in surface roughness between surveys also reduce our ability to detect change. Explicit consideration of these ambiguities, when propagated through to volume calculations, allows us to detect volume change of 87 ± 5 m3, over an area of ～ ?4900 m2, due to passage of a debris flow down a 300 m reach of the Illgraben channel in Switzerland. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
5.
Karl-Rudolf Koch 《Studia Geophysica et Geodaetica》2017,61(1):1-18
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
6.
本文研究了一种基于随机地震反演的Russell流体因子直接估算方法,该方法是一种基于蒙特卡罗的非线性反演,能够有效地融合测井资料中的高频信息,提高反演结果的分辨率.本文应用贝叶斯理论框架,首先通过测井数据计算井位置处的Russell流体因子,利用序贯高斯模拟方法(sequential Gaussian simulation,SGS)得到流体因子的先验信息;然后构建似然函数;最后利用Metropolis抽样算法对后验概率密度进行抽样,得到反演的Russell流体因子.其中对每道数据进行序贯高斯模拟时,采用一种新的逐点模拟方式,具有较高的计算速度.数值试验表明:反演结果与理论模型和实际测井数据吻合较好,具有较高的分辨率,对于判识储层含流体特征具有较好的指示作用. 相似文献
7.
地震震相拾取是地震数据自动处理的首要环节,包括了信号检测、到时估计和震相识别等过程,震相拾取的准确性直接影响到后续事件关联处理的性能,影响观测报告的质量.为了提高震相拾取的准确性,进而提高观测报告质量,本文采用深度卷积神经网络方法来解决震相拾取问题,构建了多任务卷积神经网络模型,设计了分类和回归的联合损失函数,定义了基于加权的分类损失函数,以三分量地震台站的波形数据作为输入,同时实现对震相的检测识别和到时的精确估计.利用美国南加州地震台网的200万条震相和噪声数据对模型进行训练、验证和测试,对于测试集中直达波P、S震相识别的查全率达到98%以上,到时估计的标准偏差分别为0.067s,0.082s.利用迁移学习和数据增强,将模型用于对我国东北地区台网的6个台站13000条数据的训练、验证和测试中,对该数据集P、S震相查全率分别达到91.21%、85.65%.基于迁移训练后的模型,设计了用于连续数据的震相拾取方法,利用连续的地震数据对该算法进行了实际应用测试,并与国家数据中心和中国地震局的观测报告进行比对,该方法的震相检测识别率平均可达84.5%,验证了该方法在实际应用中的有效性.本文所提出的方法展示了深度神经网络在地震震相拾取中的优异性能,为地震震相和事件的检测识别提供了新的思路. 相似文献
8.
Mohammad J. Tourian Johannes Riegger Nico Sneeuw Balaji Devaraju 《Studia Geophysica et Geodaetica》2011,55(4):627-640
Gravity measurements within the Gravity Recovery and Climate Experiment (GRACE) provide a direct measure of monthly changes
in mass over the Earth’s land masses. As such changes in mass mainly correspond to water storage changes, these measurements
allow to close the continental water balance on large spatial scales and on a monthly time scale within the respective error
bounds. When quantifying uncertainties, positive and negative peaks are detected in GRACE aggregated monthly time series (from
different data providers) that do not correspond to hydrological or hydro-meteorological signals. These peaks must be interpreted
as outliers, which carry the danger of signal degradation. In this paper an algorithm is developed to identify outliers and
replace them with hydrologically plausible values. The algorithm is based on a statistical approach in which hydrological
and hydro-meteorological signals are used to control the algorithm. The procedure of outlier detection is verified by evaluating
catchment based aggregated GRACE monthly signals with ground truth from hydrology and hydro-meteorological signals. The results
show improvement in the correlation of GRACE versus hydrometeorological and hydrological signals in most catchments. Also,
the noise level is significantly reduced over 255 largest catchments. 相似文献
9.
Woodon Jeong Constantinos Tsingas Mohammed S. Almubarak 《Geophysical Prospecting》2020,68(5):1523-1539
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
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.
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
12.
A practical method is developed for outlier detection in autoregressive modelling. It has the interpretation of a Mahalanobis distance function and requires minimal additional computation once a model is fitted. It can be of use to detect both innovation outliers and additive outliers. Both simulated data and real data re used for illustration, including one data set from water resources. 相似文献
13.
Based on the improved interaction mechanism of two-layer model, this paper proposed Pixel Component Arranging and Comparing Algorithm (PCACA) and theoretically positioning algorithm, estimated the true temperature of mixed pixel in four extreme points in combination with the measurements of dry and wet points in calibration fields and improved the reliability of positioning dry and wet line. A new two-layer energy-separation algorithm was proposed,which was simple and direct without resistance network parameters for each pixel. We also proposed a new thought about the effect of advection. The albedo of mixed pixel was also separated with PCACA. In combination with two-layer energy-separation algorithm, the net radiation of mixed pixel was separated to overcome the uncertainty of conventional energy-separation algorithm using Beer's Law. Through the validation of retrieval result, this method is proved to be feasible and operational. At the same time, the uncertainty of this algorithm was objectively analyzed. 相似文献
14.
采用当前方法测量地震形变场时,不能有效去除地震图像中存在的噪声,得到的测量结果与实际结果之间的误差较大,存在抗干扰性差和测量精准度低的问题。提出基于无人机摄影的地震形变场测量技术,对无人机摄影得到的主图像和辅图像做配准处理,采用SAR处理器处理图像数据,得到干涉图。利用SUSAN算法检测干涉图的边缘,通过K均值方法划分模板区域中存在的特征值类别,根据划分结果得到干涉图的相关参数和噪声区域,结合非线性扩散方法和SUSAN算法完成干涉图的去噪处理,依据基线估计去除干涉图中存在的平地效应,利用网络流算法完成相位解缠,获得地形相位值,绘制地震形变图,完成地震形变场的测量。实验结果表明,所提方法的抗干扰性强、测量精准度高。 相似文献
15.
剪切波分裂是分析地震各向异性的一种重要手段,常规方法是利用网格搜索获取分裂参数,再通过不同方法的测量结果对比测量结果进行质量检测,这一过程会耗费大量计算时间。本文针对这一问题提出了一种利用深度卷积神经网络对剪切波分裂进行质量检测的新方法,对使用了Resnet残差结构的深度神经网络进行训练,直接对二分量剪切波波形数据的质量进行分类。整个过程为:神经网络通过卷积层提取波形特征,计算损失函数后反向传播训练模型参数,完成迭代训练后的模型对输入波形数据正向计算自动输出类型。本文利用川西台站接收到的实际数据以及随机生成的合成数据分别对该网络进行训练,均可以获得准确的分类结果。相比于通过多种剪切波分裂方法对比测量结果的质量检测方法,基于神经网络的方法可以省略网格搜索的计算过程直接判断质量类型,在运算速度上的优势明显,并可继续通过训练提高模型的精度,为提升剪切波分裂方法在数据处理过程中的操作效率提供帮助。 相似文献
16.
The solution of the current wind retrieval algorithm for scatterometers has several wind vector ambiguities, due to the bi-harmonic
relationship between normalized backscattering cross section and the relative wind direction and the existence of the measurement
error. In order to remove the ambiguities for a unique wind field, a circular median filter approach (CMF) is usually adopted.
But under the condition for clustering distribution of the false ambiguities in some local areas, the CMF fails and thus engenders
block ambiguities, which degrade the precision of the retrieved wind field. For such a situation, a technique of identification
and removal of the block ambiguities is presented to further optimize the retrieved wind field after CMF. It is demonstrated
in experiment that this technique can identify and remove most of the block ambiguities. 相似文献
17.
Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates 总被引：1，自引：0，他引：1
Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results. 相似文献
18.
以Gassmann流体因子(Gassmann Fluid Item,GFI)为目标,提出了一种流体因子直接提取的新方法.首先,以贝叶斯反演框架为基础,将似然函数、先验信息以及Gassmann流体因子近似方程相结合,得到初始的目标函数;其次,进一步在初始目标函数中加入可变数量的点约束信息,并得到最终的目标函数;最后,通过求解该目标函数,就直接提取出了Gassmann流体因子.该方法的主要特点是不需要初始模型的参与,而是通过一个约束模型来控制提取结果的稳定性和准确性,并且可以从约束模型中选定不同数量的约束点进行约束,称为可变点约束.给出并讨论了三种常用的不同点约束模式和原则,并用模型说明了它们不同的约束效果.模型验证和实际应用结果皆以表明,该方法即使在叠前数据信噪比很低的情况下也能较好地提取出Gassmann流体因子,流体因子提取结果客观性高、稳定性好,并且能够与已知的流体解释结果很好地匹配,益于进一步推广应用. 相似文献
19.
Until the present time the ‘ rock-coal-rock’ layer sequence and offsets in coal-seams in underground coal mines have been detected with the aid of seismic waves and geoelectric measurements. In order to determine the geometrical and petrophysical parameters of the coal-seam situation, the data recorded using seismic and geoelectric methods have been inverted independently. In consequence, the inversion of partially inaccurate data resulted in a certain degree of ambiguity. This paper presents the first results of a joint inversion scheme to process underground vertical seismic profiling data, geolectric resistivity and resistance data. The joint inversion algorithm makes use of the damped least-squares method and its weighted version to solve the linearized set of equations for the seismic and geolectric unknowns. In order to estimate the accuracy and reliability of the derived geometrical and petrophysical layer parameters, both a model covariance matrix and a correlation matrix are calculated. The weighted least-squares algorithm is based on the method of most frequent values (MFV). The weight factors depend on the difference between measured data and those calculated by an iteration process. The joint inversion algorithm is tested by means of synthetic data. Compared to the damped least-squares algorithm, the MFV inversion leads to smaller estimation errors as well as lower sensitivities due to the choice of the initial model. It is shown that, compared to an independent inversion, the correlation between the model parameters is definitely reduced, while the accuracy of the parameter estimation is appreciably increased by the joint inversion process. Thus the ambiguity is significantly reduced. Finally, the joint inversion algorithm using the MFV method is applied to underground field data. The model parameters can be derived with a sufficient degree of accuracy, even in the case of noisy data. 相似文献
20.
地下地层普遍存在各向异性,忽略介质各向异性会导致速度估计不准确,成像精度下降.基于二阶声波方程的最小二乘逆时偏移忽略了介质各向异性及密度变化的影响,致使模拟地震数据与实际观测数据不匹配,影响收敛速度和反演成像质量.VTI介质一阶速度-应力方程能较好适应各向异性变密度情况,为此,本文首先从VTI介质一阶速度-应力方程出发,进行波动方程线性化;其次推导了相应的扰动方程和伴随方程,并通过伴随状态法得到梯度更新公式;最终形成基于一阶方程的LSRTM算法理论及实现流程.在实现算法的基础上,通过数值试算及成像结果对比,验证了本文算法在处理变密度和VTI介质时的有效性和优越性.偏移速度以及各向异性Thomsen参数误差的敏感性测试及误差收敛曲线对比结果进一步表明:速度及Thomsen参数对成像结果存在明显影响,其中速度敏感性最强,参数epsilon次之,参数delta的敏感性最弱. 相似文献