GPS coordinate time series measurements in Ontario and Quebec, Canada

Alinia, Hadis Samadi; Tiampo, K. F.; James, Thomas S.

doi:10.1007/s00190-016-0987-5

Cited by 6 publications

(3 citation statements)

References 75 publications

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“…Figure 2a presents the interpolated 2‐D strain rate field derived from the horizontal velocities and shows the principal extensional and contractional strain rate at each 0.5° × 0.5° grid point. In general, the main feature in the strain rate tensor agrees well with previous studies (e.g., Alinia et al., 2017; Argus & Peltier, 2010; Calais et al., 2006; George et al., 2012; Goudarzi et al., 2016; Kao et al., 2018; Kreemer et al., 2018; Mazzotti et al., 2011; Park et al., 2002; W. R. Peltier et al., 2015; Sella et al., 2007; Snay et al., 2016; Tarayoun et al., 2018; Tiampo et al., 2011) indicating the robustness of our strain rate computation. The strain rate field is interpolated across the study region to obtain a finer resolution in regions with relatively sparse GNSS stations and to emphasize regional‐scale deformation.…”

Section: Resultssupporting

confidence: 92%

Strain Accumulation and Release Rate in Canada: Implications for Long‐Term Crustal Deformation and Earthquake Hazards

et al. 2021

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To advance the understanding of crustal deformation and earthquake hazards in Canada, we analyze seismic and geodetic data sets and robustly estimate the crust strain accumulation and release rate by earthquakes. We find that less than 20% of the accumulated strain is released by earthquakes across the study area providing evidence for large‐scale aseismic deformation. We attribute this to glacial isostatic adjustment (GIA) in eastern Canada, where predictions from the GIA model account for most of the observed discrepancy between the seismic and the geodetic moment rates. In western Canada, only a small percentage (<20%) of the discrepancy can be attributed to GIA‐related deformation. We suspect that this may reflect the inaccuracy of the GIA model to account for heterogeneity in Earth structure or indicate that the present‐day effect of GIA in western Canada is limited due to the fast response of the upper mantle to the deglaciation of the Cordillera Ice Sheet. At locations of previously identified seismic source zones, we speculate that the unreleased strain is been stored cumulatively in the crust and will be released as earthquakes in the future. The Gutenberg‐Richter model predicts, however, that the recurrence interval can vary significantly in Canada, ranging from decades near plate boundary zones in the west to thousands of years in the stable continental interior. Our attempt to quantify the GIA‐induced deformation provides the necessary first step for the integration of geodetic strain rates in seismic hazard analysis in Canada.

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Section: Resultssupporting

confidence: 92%

Strain Accumulation and Release Rate in Canada: Implications for Long‐Term Crustal Deformation and Earthquake Hazards

et al. 2021

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“…In addition to wavelet analysis, some other methods have also been proposed to implement time series decomposition and errors reduction. These methods include empirical mode decomposition 23 , principal component decomposition 50,51 , Fourier transformation 52 , polynomial decomposition 10 , et al . Comparing to the other time series decomposition methods, wavelet analysis is used more widely and is considered to be of higher decomposition efficiencies in both time and frequency domains 53,54 .…”

Section: Discussionmentioning

confidence: 99%

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model

Sun

Yin

et al. 2019

Sci Rep

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Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.

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“…To date, seasonal crustal deformation detection on the basis of permanent GNSS stations has been conducted (e.g. Zhang et al 2002;Park and Won 2010;Liu et al 2015;Milyukov et al 2015;Araszkiewicz and Völksen 2016;Alinia et al 2017). Annual signals based on GPS observations were looked at by Bogusz and Figurski (2014), in which the observations from 129 permanent GPS stations from 5 consecutive years were analysed.…”

Section: Introductionmentioning

confidence: 99%

Annual crustal deformation based on GNSS observations between 1996 and 2016

Maciuk

Szombara

2018

Arab J Geosci

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The article examines the quality of reprocessed GNSS data for determination of seasonal effect in a coordinates' time series. The authors are looking for long-term changes of NEU components on the basis of weekly EPN solutions from the years 1996-2016. This is one of the first such in-depth studies using such a long time period and a large number of stations. Amplitude and its RMS values of fitting function are analysed. The analysis shows that there are significant repeatable coordinate variations: 4.7 mm, 2.9 mm and 8.3 mm for the dN, dE and dU components, respectively. The research also shows a group of coordinates' time series with very small RMS values below the coordinates' accuracy (1 mm for horizontal, 3 mm for vertical components), leading to a very good function fitting with relatively small amplitude magnitudes. The authors did not remark any connection with the above described phenomena and geographical location or type of setting up of a base station.

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GPS coordinate time series measurements in Ontario and Quebec, Canada

Cited by 6 publications

References 75 publications

Strain Accumulation and Release Rate in Canada: Implications for Long‐Term Crustal Deformation and Earthquake Hazards

Strain Accumulation and Release Rate in Canada: Implications for Long‐Term Crustal Deformation and Earthquake Hazards

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model

Annual crustal deformation based on GNSS observations between 1996 and 2016

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