scholarly journals Spatial Variations of Stochastic Noise Properties in GPS Time Series

2021 ◽  
Vol 13 (22) ◽  
pp. 4534
Author(s):  
Xiaoxing He ◽  
Machiel Simon Bos ◽  
Jean-Philippe Montillet ◽  
Rui Fernandes ◽  
Tim Melbourne ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Northwest ◽  
Spectral Index ◽  
Information Criteria ◽  
Noise Model ◽  
Power Spectral ◽  
The Pacific ◽  
Position Time Series ◽  
The Stability ◽  
Noise Models

The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well known that in the frequency domain, the noise exhibits a power-law behavior with a spectral index of around −1. By fitting various noise models to the observations and selecting the most likely one, we demonstrate that the spectral index in some regions flattens to zero at long periods while in other regions it is closer to −2. This has a significant impact on the estimated linear rate since flattening of the power spectral density roughly halves the uncertainty of the estimated tectonic rate while random walk doubles it. Our noise model selection is based on the highest log-likelihood value, and the Akaike and Bayesian Information Criteria to reduce the probability of over selecting noise models with many parameters. Finally, the noise in position time series also depends on the stability of the monument on which the GPS antenna is installed. We corroborate previous results that deep-drilled brace monuments produce smaller uncertainties than concrete piers. However, if at each site the optimal noise model is used, the differences become smaller due to the fact that many concrete piers are located in tectonic/seismic quiet areas. Thus, for the predicted performance of a new GPS network, not only the type of monument but also the noise properties of the region need to be taken into account.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

scholarly journals Noise behavior in CGPS position time series: the eastern North America case study

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
M. A. Goudarzi ◽  
M. Cocard ◽  
R. Santerre
Keyword(s):  
Time Series ◽  
Random Walk ◽  
North America ◽  
White Noise ◽  
Flicker Noise ◽  
Noise Model ◽  
Eastern North America ◽  
Position Time Series ◽  
White Noise Model ◽  
Noise Models

AbstractWe analyzed the noise characteristics of 112 continuously operating GPS stations in eastern North America using the Spectral Analysis and the Maximum Likelihood Estimation (MLE) methods. Results of both methods show that the combination ofwhite plus flicker noise is the best model for describing the stochastic part of the position time series. We explored this further using the MLE in the time domain by testing noise models of (a) powerlaw, (b)white, (c)white plus flicker, (d)white plus randomwalk, and (e) white plus flicker plus random-walk. The results show that amplitudes of all noise models are smallest in the north direction and largest in the vertical direction. While amplitudes of white noise model in (c–e) are almost equal across the study area, they are prevailed by the flicker and Random-walk noise for all directions. Assuming flicker noise model increases uncertainties of the estimated velocities by a factor of 5–38 compared to the white noise model.

Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation

2014 ◽  
Vol 151 ◽  
pp. 114-123 ◽  
Author(s):  
Damien Sulla-Menashe ◽  
Robert E. Kennedy ◽  
Zhiqiang Yang ◽  
Justin Braaten ◽  
Olga N. Krankina ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Northwest ◽  
Forest Disturbance ◽  
Temporal Segmentation ◽  
The Pacific

scholarly journals Search of weights in the problem of finite-rank signal estimation in presence of noise

2021 ◽  
Vol 8 (3) ◽  
pp. 417-429
Author(s):  
Nikita K. Zvonarev ◽  
Keyword(s):  
Time Series ◽  
Numerical Simulations ◽  
Covariance Matrix ◽  
Finite Rank ◽  
Noise Model ◽  
Estimation Accuracy ◽  
Signal Estimation ◽  
Series Approximation ◽  
Inverse Covariance Matrix ◽  
Noise Models

The problem of weighted finite-rank time-series approximation is considered for signal estimation in “signal plus noise” model, where the inverse covariance matrix of noise is (2p+1)-diagonal. Finding of weights, which improve the estimation accuracy, is examined. An effective method for the numerical search of the weights is constructed and proved. Numerical simulations are performed to study the improvement of the estimation accuracy for several noise models.

Noise Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
White Noise ◽  
Autocorrelation Function ◽  
Model Building ◽  
Moving Average ◽  
Information Criteria ◽  
Noise Model ◽  
Adequate Model ◽  
Noise Modeling ◽  
Modeling Strategy

Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

A streptomycete antagonist to Phellinus weirii, Fomes annosus, and Phytophthora cinnamomi

1980 ◽  
Vol 26 (5) ◽  
pp. 583-587 ◽  
Author(s):  
Sharon L. Rose ◽  
Ching-Yan Li ◽  
Anita Stiebrs Hutchins
Keyword(s):  
Biological Control ◽  
Pacific Northwest ◽  
Phytophthora Cinnamomi ◽  
Culture Media ◽  
Antimicrobial Substance ◽  
Consistent Effect ◽  
Root Pathogens ◽  
The Pacific ◽  
Phellinus Weirii ◽  
The Stability

An actinomycete isolated from the rhizoplane of nitrogen-fixing nodules of Ceanothus velutinus was identified as a variety of Streptomyces griseoloalbus. Streptomyces griseoloalbus is a strong antagonist to three destructive root pathogens, Phellinus weirii, Fomes annosus, and Phytophthora cinnamomi, inhibiting all three on several culture media and preventing establishment of F. annosus on hemlock wood disks. The stability and longevity of the antimicrobial substance produced by it, its consistent effect on the pathogens on all substrates, its ability to colonize wood, and its ability to grow at 10 °C suggest biological control possibilities for this organism in the Pacific Northwest.

scholarly journals Efficient modeling of correlated noise

2020 ◽  
Vol 638 ◽  
pp. A95
Author(s):  
J.-B. Delisle ◽  
N. Hara ◽  
D. Ségransan
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Complex Analysis ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Noise Model ◽  
Correlated Noise ◽  
Dataset Size ◽  
Noise Models ◽  
Efficient Modeling

Correlated noise affects most astronomical datasets and to neglect accounting for it can lead to spurious signal detections, especially in low signal-to-noise conditions, which is often the context in which new discoveries are pursued. For instance, in the realm of exoplanet detection with radial velocity time series, stellar variability can induce false detections. However, a white noise approximation is often used because accounting for correlated noise when analyzing data implies a more complex analysis. Moreover, the computational cost can be prohibitive as it typically scales as the cube of the dataset size. For some restricted classes of correlated noise models, there are specific algorithms that can be used to help bring down the computational cost. This improvement in speed is particularly useful in the context of Gaussian process regression, however, it comes at the expense of the generality of the noise model. In this article, we present the S + LEAF noise model, which allows us to account for a large class of correlated noises with a linear scaling of the computational cost with respect to the size of the dataset. The S + LEAF model includes, in particular, mixtures of quasiperiodic kernels and calibration noise. This efficient modeling is made possible by a sparse representation of the covariance matrix of the noise and the use of dedicated algorithms for matrix inversion, solving, determinant computation, etc. We applied the S + LEAF model to reanalyze the HARPS radial velocity time series of the recently published planetary system HD 136352. We illustrate the flexibility of the S + LEAF model in handling various sources of noise. We demonstrate the importance of taking correlated noise into account, and especially calibration noise, to correctly assess the significance of detected signals.

scholarly journals Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis

2019 ◽  
Vol 11 (4) ◽  
pp. 386 ◽  
Author(s):  
Wenhao Li ◽  
Fei Li ◽  
Shengkai Zhang ◽  
Jintao Lei ◽  
Qingchuan Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
White Noise ◽  
Flicker Noise ◽  
Component Analysis ◽  
Independent Component ◽  
Noise Model ◽  
Residual Time ◽  
Noise Characteristics ◽  
Noise Models

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

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