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Geodesy: General theory and methodology 2015–2018

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The summary of research activities concerning general theory and methodology performed in Poland in the period of 2015–2018 is presented as a national report for the 27th IUGG (International Union of Geodesy and Geophysics) General Assembly. It contains the results of research on new or improved methods and variants of robust parameter estimation and their application, especially to control network analysis. Reliability analysis of the observation system and an integrated adjustment approach are also given. The identifiability (ID) index as a new measure for minimal detectable bias (MDB) in the observation system of a network, has been introduced. A new method of covariance function parameter estimation in the least squares collocation has been developed. The robustified version of the Shift-Msplit estimation, termed as Shift-M*split estimation, which enables estimation of parameter differences (robustly), without the need of prior estimation of the parameters, has been introduced. Results on the analysis of geodetic time series, particularly Earth orientation parameter time series, geocenter time series, permanent station coordinates and sea level variation time series are also provided in this review paper. The entire bibliography of related works is provided in the references.
Rocznik
Strony
145--162
Opis fizyczny
Bibliogr. 45 poz.
Twórcy
  • Wroclaw University of Environmental and Life Sciences Institute of Geodesy and Geoinformatics 53 Grunwaldzka St., 50-357 Wroclaw, Poland
  • University of Agriculture in Krakow Faculty of Environmental Engineering and Land Surveying 24/28 Al. Mickiewicza, 30-059 Kraków, Poland
autor
  • AGH University of Science and Technology Department of Integrated Geodesy and Cartography 30 Al. Mickiewicza, 30-059 Krakow, Poland
Bibliografia
  • [1] Baarda,W. (1968). A testing procedure for use in geodetic networks. Publ. Geod. New Ser. 2. Netherlands Geodetic Commission, Delft. 97p.
  • [2] Bogusz, J., Klos, A., Figurski, M. and Kujawa, M. (2016). Investigation of long-range dependencie in the stochastic part of daily GPS solutions. Surv. Rev., 48, 140–147. DOI: 10.1179/1752270615Y.0000000022.
  • [3] Borkowski, A. and Kosek W. (2015). Theoretical geodesy. Geod. Cartogr., Special Issue, 64, 99–113. DOI: 10.1515/geocart-2015-0015.
  • [4] Brzezinski, A., Józwik, M., Kaczorowski, M., Kalarus, M., Kasza, D., Kosek, W., Nastula, J., Szczerbowski, Z., Winska, M., Wronowski, R., Zdunek, R. and Zieli´nski, J.B. (2016). Geodynamic research at the department of planetary geodesy, SRC PAS. Rep. Geod. Geoinf., 100, 131–147. DOI: 10.1515/rgg-2016-0011.
  • [5] Cellmer, S. (2015). Least fourth powers: optimisation method favouring outliers. Surv. Rev., 47, 411–417. DOI: 10.1179/1752270614Y.0000000142.
  • [6] Cymerman, M., Duchnowski, R. and Kopiejczyk, A. (2016). Selection of initial parameters in R-estimates applied to deformation analysis in leveling networks. J. Surv. Eng., 142, 1–6. DOI: 10.1061/(ASCE)SU.1943-5428.0000151.
  • [7] Duchnowski, R. and Wi´sniewski, Z. (2017). Accuracy of the Hodges–Lehmann estimates computed by applying Monte Carlo simulations. Acta Geod. Geophys., 52, 511–525. DOI: 10.1007/s40328-016-0186-0.
  • [8] Gruszczynska, M., Klos, A., Rosat, S. and Bogusz, J. (2017). Detecting spatial dependencies in GPS position time series by using Multichannel Singular Spectrum Analysis. Acta Geodyn. Geomater., 14, 267–278. DOI: 10.13168/AGG.2017.0010.
  • [9] Jarmołowski, W. (2015). Least-squares collocation with uncorrelated heterogeneous noise estimated by restricted maximum likelihood. J. Geod., 89, 577–589. DOI: 10.1007/s00190-015-0800-x.
  • [10] Jarmołowski, W. (2017). Fast estimation of covariance parameters in least-squares collocation by Fisher scoring with Levenberg–Marquardt optimization. Surv. Geophys., 38, 701–725. DOI: 10.1007/s10712-017-9412-8.
  • [11] Jurecka, M., Niedzielski, T. and Migo´n, P. (2016). A novel GIS-based tool for estimating present-day ocean reference depth using automatically processed gridded bathymetry data. Geomorph., 260, 91–98. DOI: 10.1016/jgeomorph.2015.05.021.
  • [12] Kaczmarek, A. and Kontny, B. (2018a). Estimates of seasonal signals in GNSS time series and environmental loading models with iterative Least-squares Estimation (iLSE) approach. Acta Geodyn. Geomater., 15, 131–141. DOI: 10.13168/AGG.2018.0009.
  • [13] Kaczmarek, A. and Kontny, B. (2018b). Identification of the noise model in the time series of GNSS stations coordinates using wavelet analysis. Rem. Sens., 10, 1611–1620. DOI: 10.3390/rs10101611.
  • [14] Kadaj, R. (2016a). The combined geodetic network adjusted on the reference ellipsoid – a comparison of three functional models for GNSS observations. Geodesy and Cartography 65, 229–257. DOI: 10.1515/geocart-2016-0013.
  • [15] Kadaj, R. (2016b). Empirical methods of reducing the observations in geodetic networks. Geodesy and Cartography, 65, 13–40. DOI: 10.1515/geocart-2016-0001.
  • [16] Klos, A., Bogusz, J., Figurski, M., Gruszczynska, M. and Gruszczynski, M. (2015). Investigation of noises in the EPN weekly time series. Acta Geodyn. Geomater., 12, 117–126. DOI: 10.13168/AGG.2015.0010.
  • [17] Klos A., Bogusz, J., Figurski, M. and Kosek, W. (2016a). On the handling of outliers in the GNSS time series by means of the noise and probability analysis. In: Rizos C., Willis P. (eds.) IAG 150 Years Symposia, 143, 657–664. Springer Cham. DOI: 10.1007/1345_2015_78.
  • [18] Klos, A., Bogusz, J., Figurski, M. and Gruszczy´nski, M. (2016b). Error analysis for European IGS stations. Stud Geophys Geod., 60, 17–34, DOI: 10.1007/s11200-015-0828-7.
  • [19] Klos, A., Bogusz, J. and Moreaux, G. (2018a). Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014. J. Geod., 92, 743–763, DOI: 10.1007/s00190-017-1092-0.
  • [20] Klos, A., Hunegnaw, A., Teferle, F.N., Abraha, K.E., Ahmed, F. and Bogusz, J. (2018b). Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions. GPS Solut., 22: 51. DOI: 10.1007/s10291-018-0717-y.
  • [21] Klos, A., Olivares, G., Teferle, F.N., Hunegnaw, A. and Bogusz, J. (2018c). On the combined effect of periodic signals and colored noise on velocity uncertainties. GPS Solut., 22:1. DOI: 10.1007/s10291-017-0674-x.
  • [22] Klos, A., Bos, M.S., Fernandes, R.M.S. and Bogusz, J. (2019). Noise-dependent adaption of the Wiener filter for the GPS position time series. Math. Geosci., 51, 53–73. DOI: 10.1007/s11004-018-9760-z.
  • [23] Kosek, W., Niedzielski, T., Popinski, W., Zbylut-Górska, M. and Wn˛ek, A. (2015a). Variable seasonal and subseasonal oscillations in sea level anomaly data and their impact on prediction accuracy. In: Freymueller, J.T. (ed.) IAG: Symposia, 142, 1–6. DOI: 10.1007/1345_2015_74.
  • [24] Kosek, W., Wn˛ek, A. and Zbylut-Górska, M. (2015b). Corrections to sea level anomalies data due to geocenter motion. Geomat., Landmanagement Landsc., 2, 33–44. DOI: 10.15576/GLL/2015.2.33.
  • [25] Kwasniak, M. (2015). Identification of the reference base for horizontal displacements by “all-pairs method.” Rep. Geod. Geoinf., 98, 72–84. DOI: 10.2478/rgg-2015-0007.
  • [26] Niedzielski, T., Jurecka, M. and Migo´n, P. (2016). Semi-empirical oceanic depth-age relationship inferred from bathymetric curve. Pure Appl. Geophys., 173, 1829–1840. DOI: 10.1007/s00024-015-1204-9.
  • [27] Niedzielski, T. (2017). Basic prediction methods in marine sciences. In: Green D.R., Payne J. (eds.), Marine and Coastal Resource Management – Principles and Practice. 1st Ed. Routledge, Taylor and Francis Group, 121–141.
  • [28] Nowak, E. and Odziemczyk, W. (2018). Impact analysis of observation coupling on reliability indices in a geodetic network. Rep. Geod. Geoinf., 106, 1–7. DOI: 10.2478/rgg-2018-0008.
  • [29] Nowel, K. (2015). Robust M-estimation in analysis of control network deformations: classical and new method. J. Surv. Eng., 141, 1–10. DOI: 10.1061/(ASCE)SU.1943-5428.0000144.
  • [30] Nowel, K. (2016a). Application of Monte Carlo method to statistical testing in deformation analysis based on robust M-estimation. Surv. Rev., 48, 212–223. DOI: 10.1179/1752270615Y.
  • [31] Nowel, K. (2016b). Investigating efficacy of robust M-estimation of deformation from observation differences. Surv. Rev., 48, 21–30. DOI: 10.1080/00396265.2015.109785.0000000026.
  • [32] Osada, E., Borkowski, A., Kurpinski, G., Oleksy, M. and Seta, M. (2017a). Fitting a precise levelling network to control points using a modified robust Huber’s mean error function. J. Surv. Eng., 143, 1–6. DOI: 10.1061/(ASCE)SU.1943-5428.0000201.
  • [33] Osada, E., Owczarek-Wesołowska, M., Ficner, M. and Kurpinski, G. (2017b). TotalStation/GNSS/EGM integrated geocentric positioning method. Surv. Rev., 49, 1–6, DOI: 10.1080/00396265. 2016.1151969.
  • [34] Osada, E., Sosnica, K., Borkowski, A., Owczarek-Wesołowska, M. and Gromczak, A. (2017c). A direct georeferencing method for terrestrial laser scanning using GNSS data and the vertical deflection from global earth gravity models. Sensors, 17, 1–12. DOI: 10.3390/s17071489.
  • [35] Osada, E., Borkowski, A., Sosnica, K., Kurpinski, G., Oleksy, M. and Seta, M. (2018). Robust fitting of a precise planar network to unstable control points using M-estimation with a modified Huber function. J. Spat. Sci., 63, 35–47, DOI: 10.1080/14498596.2017.1311238.
  • [36] Pachelski, W. and Postek, P. (2016). Optimization of observation plan based on the stochastic characteristics of the geodetic network. Rep. Geod. Geoinf., 101, 16–26. DOI: 10.1515/rgg-2016-0018.
  • [37] Pedzich, P. (2017). Equidistant map projections of a triaxial ellipsoid with the use of reduced coordinates. Geodesy and Cartography, 66, 271–290. DOI: 10.1515/geocart-2017-0021.
  • [38] Prószynski, W. (2015). Revisiting Baarda’s concept of minimal detectable bias with regard to outlier identifiability. J. Geod., 89, 993–1003. DOI: 10.1007/s00190-015-0828-y.
  • [39] Prószyński, W. and Kwasniak, M. (2016). An attempt to determine the effect of increase of observation correlations on detectability and identifiability of a single gross error. Geodesy and Cartography, 65, 313–333. DOI: 10.1515/geocart-2016-0018.
  • [40] Prószynski, W. and Kwasniak, M. (2018). Analytic tools for investigating the structure of network reliability measures with regard to observation correlations. J. Geod., 92, 321–332. DOI: 10.1007/s00190-017-1064-4.
  • [41] Swierczynska, M., Mizinski, B. and Niedzielski, T. (2016). Comparison of predictive skills offered by Prognocean, Prognocean Plus and MyOcean real-time sea level forecasting systems. Ocean Eng., 113, 44–56. DOI: 10.1016/j.oceaneng.2015.12.023.
  • [42] Wilgan, K. (2015). Zenith total delay short-term statistical forecasts for GNSS Precise Point Positioning. Acta Geodyn. Geomater., 12, 335–343. DOI: 10.13168/AGG.2015.0035.
  • [43] Wisniewski, Z. (2017).Mp estimation applied to platykurtic sets of geodetic observations. Geod. Cartogr., 66, 117–135. DOI: 10.1515/geocart-2017-0001.
  • [44] Wisniewski, Z. and Zienkiewicz, M.H. (2016). Shift-M_split Estimation in Deformation Analyses, J. Surv. Eng., 142, 1–13, DOI: 10.1061/(ASCE)SU.1943-5428.0000183.
  • [45] Zienkiewicz, M.H. (2015). Application of Msplit estimation to determine control points displacements in networks with unstable reference system. Surv. Rev., 47, 174–180. DOI: 10.1179/1752270614Y.0000000105.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c719c9e1-152d-40b5-aa1c-a128052d93fa
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