Reference frame access under the effects of great earthquakes: a least squares collocation approach for non-secular post-seismic evolution

Gómez, D.; Piñón, D. A.; Smalley, Robert; Bevis, M. G.; Cimbaro, S.; Lenzano, Luis; Barón, Jorge

doi:10.1007/s00190-015-0871-8

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Nevertheless, a reliable deformation modelling is not yet guaranteed. Some authors suggest the implementation of geodynamic models to predict the pointwise coordinate changes caused by coseismic and post-seismic effects (see e.g., Snay et al 2013;Bevis and Brown 2014;Gómez et al 2015). Since these models rely on hypotheses about the physical properties of the upper Earth crust, different hypotheses produce different results as demonstrated by e.g., Li et al (2017).…”

Section: Discussionmentioning

confidence: 99%

Geodetic Monitoring of the Variable Surface Deformation in Latin America

Sánchez

Drewes

2020

International Association of Geodesy Symposia

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Based on 24 years of high-level GNSS data analysis, we present a sequence of crustal deformation models showing the varying surface kinematics in Latin America. The deformation models are inferred from GNSS station horizontal velocities using a leastsquares collocation approach with empirically determined covariance functions. The main innovation of this study is the assumption of continuous surface deformation. We do not introduce rigid microplates, blocks or slivers which enforce constraints on the deformation model. Our results show that the only stable areas in Latin America are the Guiana, Brazilian and Atlantic shields; the other tectonic entities, like the Caribbean plate and the North Andes, Panama and Altiplano blocks are deforming. The present surface deformation is highly influenced by the effects of seven major earthquakes:

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Section: Discussionmentioning

confidence: 99%

Geodetic Monitoring of the Variable Surface Deformation in Latin America

Sánchez

Drewes

2020

International Association of Geodesy Symposia

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“…A unique standard terrestrial reference frame (TRF) is fundamental to ensure interoperability and consistency of geodetic products and to adequately exploit various measurements collected by ground-based sensors, or via artificial satellites for Earth science and geodesy applications [1][2][3], including plate tectonics [4,5], co-seismic and post-seismic deformations [6,7], global geophysical fluid dynamics [8,9], ice melting [10], accurately determining point positions, and the rate of sea level rise [11,12], etc.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Improvement in Observation Precision of GNSS, SLR, VLBI, and DORIS Inputs from ITRF2014 to ITRF2020 Using TRF Stacking Methods

Zhang,

Huang,

Lian

et al. 2024

Remote Sensing

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International terrestrial reference frame (ITRF) input data, generated by Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radiopositioning integrated by satellite (DORIS) combination centers (CCs), are considered to be relatively high-quality and accurate solutions. Every few years, these input data are submitted to the three ITRS combination centers, namely Institut Géographique National (IGN), Deutsches Geodätisches Forschungsinstitut at the Technische Universität München (DGFI-TUM), and Jet Propulsion Laboratory (JPL), to establish a multi-technique combined terrestrial reference frame (TRF). Generally, these solutions have undergone three rounds of outlier removal: the first at the technique analysis centers during solution generations and the second during the technique-specific combination by the CCs; ITRS CCs then perform a third round of outlier removal and preprocessing during the multi-technique combination of TRFs. However, since the primary objective of CCs is to release the final TRF product, they do not emphasize the publication of analytical preprocessing results, such as the outlier rejection rate. In this paper, our specific focus is on assessing the precision improvement of ITRF input data from 2014 to 2020, which includes evaluating the accuracy of coordinates, the datum accuracy, and the precision of the polar motions, for all four techniques. To achieve the above-mentioned objectives, we independently propose a TRF stacking approach to establish single technical reference frameworks, using software developed by us that is different from the ITRF generation. As a result, roughly 0.5% or less of the SLR observations are identified as outliers, while the ratio of DORIS, GNSS, and VLBI observations are below 1%, around 2%, and ranging from 1% to 1.2%, respectively. It is shown that the consistency between the SLR scale and ITRF has improved, increasing from around −5 mm in ITRF2014 datasets to approximately −1 mm in ITRF2020 datasets. The scale velocity derived from fitting the VLBI scale parameter series with all epochs in ITRF2020 datasets differs by approximately 0.21 mm/year from the velocity obtained by fitting the data up to 2013.75 because of the scale drift of VLBI around 2013. The decreasing standard deviations of the polar motion parameter (XPO, YPO) offsets between Stacking TRFs and 14C04 (20C04) indicate an improvement in the precision of polar motion observations for all four techniques. From the perspective of the weighted root mean square (WRMS) in station coordinates, since the inception of the technique, the station coordinate WRMS of DORIS decreased from 30 mm to 5 mm for X and Y components, and 25 mm to 5 mm for the Z component; SLR WRMS decreased from 20 mm to better than 10 mm (X, Y and Z); GNSS WRMS decreased from 4 mm to 1.5 mm (X and Y) and 5 mm to 2 mm (Z); while VLBI showed no significant change.

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“…For many years various versions of ITRF were defined and realized using trajectory models that did not include annual or semiannual periodic displacements. As oscillatory displacements were resolved with greater accuracy, it was recognized that adding seasonal terms to station trajectory models increases the stability and accuracy of station velocity estimates (e.g., Blewitt and Lavallée 2002;Dong et al 2002;Gómez et al 2015). This stability and accuracy increase led Bevis and Brown (2014) to suggest that new classes of trajectory models that incorporated annual periodic displacements or oscillations (and other features) should be used to define RFs, not just to characterize the displacements of stations expressed in those frames.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the consistency between regional and global reference frames utilizing inheritance of seasonal displacement parameters

Gómez

Bevis

Caccamise

2022

J Geod

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Although modern global geometric reference frames (GRFs) such as the International Terrestrial Reference Frame (ITRF) can be used anywhere on Earth, regional reference frames (RRFs) are still used to densify geodetic control and optimize solutions for continental-scale areas and national purposes. Such RRFs can be formed by densifying the ITRF, utilizing GPS / GNSS stations common to both the ITRF and the RRF. It is possible to attach a RRF to a GRF by ensuring that some or all of the coefficients of the trajectory models in the RRF are ‘inherited’ from the trajectory models that define the GRF. This can be done on an epoch-by-epoch basis, or (our preference) via transformations that operate simultaneously in space and time. This paper documents inconsistencies in the densification of ITRF that arise when the common stations’ trajectory models ignore periodic displacements. This results in periodic coordinate biases in the RRF. We describe a generalized procedure to minimize this inconsistency when realizing any RRF aligned to the ITRF or any other ‘primary’ frame. We show the method used to realize the Argentine national frame Posiciones Geodésicas Argentinas (POSGAR) and discuss our results. Discrepancies in the periodic motion amplitudes in the vertical were reduced from 4 mm to less than 1 mm for multiple stations after applying our technique. We also propose adopting object-oriented programming terminology to describe the relationship between different reference frames, such as a regional and a global frame. This terminology assists in describing and understanding the hierarchy in geodetic reference frames.

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Reference frame access under the effects of great earthquakes: a least squares collocation approach for non-secular post-seismic evolution

Cited by 5 publications

References 22 publications

Geodetic Monitoring of the Variable Surface Deformation in Latin America

Geodetic Monitoring of the Variable Surface Deformation in Latin America

Assessment of the Improvement in Observation Precision of GNSS, SLR, VLBI, and DORIS Inputs from ITRF2014 to ITRF2020 Using TRF Stacking Methods

Maximizing the consistency between regional and global reference frames utilizing inheritance of seasonal displacement parameters

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