Updated GNSS velocity solution in the Nordic and Baltic countries with a semi-automatic offset detection method

Lahtinen, Sonja; Jivall, Lotti; Häkli, Pasi; Nordman, Maaria

doi:10.1007/s10291-021-01194-z

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Similar results can be found in Gazeaux et al (2013). Most recently, Lahtinen et al (2021) proposed a semiautomatic offset detection method and achieved a detection performance of 49% for TP, 15% for FN, and 35% for FP, compared to manual detection results. Generally, Lahtinen et al (2021) have attained superior outcomes with real datasets until now.…”

Section: Introductionsupporting

confidence: 80%

“…They conclude that ignoring flicker noise existing in the time series will lead to lower offset detection performance. However, Lahtinen et al (2021) find that small-size offsets, behaving like flicker noise, can be omitted if flicker noise model is used. Consequently, they only applied the white noise model.…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, Lahtinen et al (2021) proposed a semiautomatic offset detection method and achieved a detection performance of 49% for TP, 15% for FN, and 35% for FP, compared to manual detection results. Generally, Lahtinen et al (2021) have attained superior outcomes with real datasets until now. However, their FP rate remains relatively high, and the data they used is from 268 stations in Nordic Geodetic Commission (NKG) rather than global network.…”

Section: Introductionmentioning

confidence: 99%

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Automated offset detection approaches: case study in IGS Repro2 and 3

Zhang,

Lian,

Huang

et al. 2024

GPS Solut

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Due to their high false positive detection rates, traditional automated offset detections on GNSS station coordinate time series still cannot fully replace manual detections in practical applications and need to be improved. This work proposes preliminary and enhanced automated offset detection approaches for IGS Repro2 and Repro3 datasets. Their manually detected offset recordings in ITRF2014 and ITRF2020 are used as evaluation criteria. In the preliminary approaches, stochastic models based on covariance matrix, white noise model, and white noise plus flicker noise model of both univariate and multivariate are studied.Although a somewhat "good" performance is achieved, the preliminary automated detections still lead to many false positive detections. Thus, based on the preliminary approaches, and ancillary data, an enhanced detection approach is proposed. Enhanced detections significantly reduce 56%~80% false positive detections compared to preliminary approaches. As a result, for Repro3, the optimal overall performance is attained with true positive, false positive, and false negative rates (TP, FP, FN) of (0.57, 0.25, 0.18), along with a detection rate of 75%; for Repro2, the rates are (0.58, 0.20, 0.22), accompanied by a 73% detection rate.According to its "good" performance mentioned above, the current enhanced approach may serve as a supplementary or reference to manual detection, although still not being perfect. Furthermore, twenty manually detected unknown offsets in ITRF2020 are found to correspond to some known events (thirteen earthquakes and seven equipment changes); thirty-four automated detections that correspond to known events but are not collected in ITRF2020 are manually checked as offsets (fourteen earthquakes and twenty equipment changes).

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Section: Introductionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated offset detection approaches: case study in IGS Repro2 and 3

Zhang,

Lian,

Huang

et al. 2024

GPS Solut

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“…Step terms are caused mainly by instrument replacement and external environmental changes (Lahtinen et al 2022). We identify the times at which steps occur from the step event log file, calculate the difference between the medians of the 30 days of time series data before and after the identified time, and recover the difference in the GNSS time series data if it is greater than the threshold.…”

Section: A) Step Termsmentioning

confidence: 99%

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Yan,

Dai,

et al. 2024

Earth Planets Space

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Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios. Graphical Abstract

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“…As was mentioned in the introduction, the GNSS stations are often used to estimate GNSS velocities, for example, to remove VLM from the relative TG-based sea level time series. The GNSS-CORS that belong to the national networks have relatedly been included in relevant research to estimate GNSS velocities and compile VLM models [22,[74][75][76]. However, as further discussed in the introduction, the GNSS records/measurements may similarly require consideration for VLM in marine applications since the measurements are forced to the reference epoch t 0 (i.e., the coordinates of CORS are fixed to the defining epoch).…”

Section: Treatment Of Marine Gnss Measurements For Vertical Land Motionmentioning

confidence: 99%

Treatment of Tide Gauge Time Series and Marine GNSS Measurements for Vertical Land Motion with Relevance to the Implementation of the Baltic Sea Chart Datum 2000

et al. 2022

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Tide gauge (TG) time series and GNSS measurements have become standard datasets for various scientific and practical applications. However, the TG and geodetic networks in the Baltic Sea region are deforming due to vertical land motion (VLM), the primary cause of which is the glacial isostatic adjustment. Consequently, a correction for VLM, either obtained from a suitable VLM model or by utilizing space-geodetic techniques, must be applied to ensure compatibility of various data sources. It is common to consider the VLM rate relative to an arbitrary reference epoch, but this also yields that the resulting datasets may not be directly comparable. The common height reference, Baltic Sea Chart Datum 2000 (BSCD2000), has been initiated to facilitate the effective use of GNSS methods for accurate navigation and offshore surveying. The BSCD2000 agrees with the current national height realizations of the Baltic Sea countries. As TGs managed by national authorities are rigorously connected to the national height systems, the TG data can also be used in a common system. Hence, this contribution aims to review the treatment of TG time series for VLM and outline potential error sources for utilizing TG data relative to a common reference. Similar consideration is given for marine GNSS measurements that likewise require VLM correction for some marine applications (such as validating marine geoid models). The described principles are illustrated by analyzing and discussing numerical examples. These include investigations of TG time series and validation of shipborne GNSS determined sea surface heights. The latter employs a high-resolution geoid model and hydrodynamic model-based dynamic topography, which is linked to the height reference using VLM corrected TG data. Validation of the presented VLM corrected marine GNSS measurements yields a 1.7 cm standard deviation and –2.7 cm mean residual. The estimates are 1.9 cm and –10.2 cm, respectively, by neglecting VLM correction. The inclusion of VLM correction thus demonstrates significant improvement toward data consistency. Although the focus is on the Baltic Sea region, the principles described here are also applicable elsewhere.

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Updated GNSS velocity solution in the Nordic and Baltic countries with a semi-automatic offset detection method

Cited by 7 publications

References 18 publications

Automated offset detection approaches: case study in IGS Repro2 and 3

Automated offset detection approaches: case study in IGS Repro2 and 3

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Treatment of Tide Gauge Time Series and Marine GNSS Measurements for Vertical Land Motion with Relevance to the Implementation of the Baltic Sea Chart Datum 2000

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