A new peak fitting method for 1D solid-state 29Si NMR spectra based on singular spectrum analysis

2019 ◽  
Vol 18 (01) ◽  
pp. 1941014
Author(s):  
Guiliang Li ◽  
Changjun Li ◽  
Nan Wei
Keyword(s):  
Molecular Structure ◽  
Spectrum Analysis ◽  
Nmr Spectra ◽  
Singular Spectrum Analysis ◽  
Embedding Dimension ◽  
Characteristic Peak ◽  
Gaussian Fitting ◽  
Singular Spectrum ◽  
Peak Fitting ◽  
Fitting Method

[Formula: see text]Si Nuclear Magnetic Resonance (NMR) can measure the molecular structure of silicate in oilfield reinjection water. However, noise in [Formula: see text]Si NMR spectra (NMRS) affects the determination of silicate molecular structure type. To solve this problem, a new peak fitting method (Two-step Greedy-Singular Spectrum Analysis-Gaussian Fitting Method, TSG-SSA-GFM) is proposed in this paper. This method first uses TSG to determine the embedding dimension, then uses SSA to determine the characteristic peak position. Finally, GFM is used to calculate the molar ratio of characteristic peaks. The results show that TSG can quickly determine the embedding dimension and reduce computation by at least 50% vs. the global ergodic method. The mean deviation of characteristic peak positions determined by SSA is 0.07 ppm, while Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) cannot determine characteristic peaks of [Formula: see text]Si NMRS containing overlapping peak. The average [Formula: see text]-squared of Gaussian fitting of [Formula: see text]Si NMRS is 98.4% while Lorentzian is 90.6%. Therefore, this study provides an important method for quantitative analysis of [Formula: see text]Si NMRS.

scholarly journals Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 697 ◽  
Author(s):  
Shanzhi Xu ◽  
Hai Hu ◽  
Linhong Ji ◽  
Peng Wang
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Eeg Signal ◽  
Embedding Dimension ◽  
Singular Spectrum ◽  
Selection For

Singular spectrum analysis for an automated solvent artifact removal and baseline correction of 1D NMR spectra

2011 ◽  
Vol 210 (2) ◽  
pp. 177-183 ◽  
Author(s):  
Silvia De Sanctis ◽  
Wilhelm M. Malloni ◽  
Werner Kremer ◽  
Ana M. Tomé ◽  
Elmar W. Lang ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Nmr Spectra ◽  
Singular Spectrum Analysis ◽  
Baseline Correction ◽  
Artifact Removal ◽  
Singular Spectrum

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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