Comparison of the KTH and remove–compute–restore techniques to geoid modelling in a mountainous area

Abbak, Ramazan Alpay; Erol, Bihter; Ustun, Aydin

doi:10.1016/j.cageo.2012.05.019

Cited by 24 publications

(7 citation statements)

References 22 publications

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“…The determination of geoid (in this case, marine geoid) is carried out from the least-squares modification of Stokes' formula, which is commonly called as KTH method developed at the Royal Institute of Technology [17], [18], [19]. KTH method is selected as an approach to determine the geoid model due to the mode of the system used in determining the optimum parameters in geoid computation, which crucial in providing a good accuracy of a geoid model.…”

Section: Fig 2 -United Nations On Sustainable Development Goals [7]mentioning

confidence: 99%

Assessment of Global Geopotential Models for Modelling Malaysia Marine Geoid

Yahaya¹,

Din²,

Omar³

et al. 2022

IJIE

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The evaluation towards global geopotential models represents a significant part in modelling the localised Marine Geoid. The marine geoid provides the vertical reference information in Marine Spatial Data Infrastructures (MSDI) development response to United Nations Sustainable Development Goals 14 for the sustainable development in marine environment. The main purpose of this study is to select the best model from both combined missions and satellite-only missions for the Malaysian region. The gravity anomaly field from 30 global models were exclusively calculated over the selected study area within 11 years period-time. Afterwards, each dataset was extracted from the ICGEM server to evaluate with the airborne-derived gravity anomaly from the Department of Surveying and Mapping, Malaysia. The internal accuracy, root mean square error (RMSE) and differences between every model and airborne data were computed. The result indicates GGM-derived gravity anomaly for the best combined mission is GECO with RMSE of 8.44 mGal and the standard deviation value of 28.034 mGal. While, the model from Gravity field and steady state Ocean Circulation Explorer (GOCE) namely, the GO_CONS_GCF_2_DIR_R5 is the best for the satellite-only mission with RMSE of 17.43 mGal and the standard deviation value of 22.828 mGal. As a conclusion, GECO model is preferred as the best fit for determining the marine geoid as it has the lowest RMSE value between both mission and the maximum degree of 2109ocoverage. The finding can assist in development of marine geoid for modelling precise surface elevation.

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Section: Fig 2 -United Nations On Sustainable Development Goals [7]mentioning

confidence: 99%

Assessment of Global Geopotential Models for Modelling Malaysia Marine Geoid

Yahaya¹,

Din²,

Omar³

et al. 2022

IJIE

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“…Sjöberg. The LSMSA method was applied for regional geoid modelling in many areas worldwide such as the Nordic-Baltic area (Ellmann 2004;Ågren et al 2016), Sweden (Ågren et al 2009), France (Yildiz et al 2012), Turkey (Abbak et al 2012) andCroatia (Varga 2018).…”

Section: Gravimetric Geoid Modellingmentioning

confidence: 99%

Contribution of GRAV-D airborne gravity to improvement of regional gravimetric geoid modelling in Colorado, USA

Varga¹,

Pitoňák

Novák

et al. 2021

J Geod

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This paper studies the contribution of airborne gravity data to improvement of gravimetric geoid modelling across the mountainous area in Colorado, USA. First, airborne gravity data was processed, filtered, and downward-continued. Then, three gravity anomaly grids were prepared; the first grid only from the terrestrial gravity data, the second grid only from the downward-continued airborne gravity data, and the third grid from combined downward-continued airborne and terrestrial gravity data. Gravimetric geoid models with the three gravity anomaly grids were determined using the least-squares modification of Stokes’ formula with additive corrections (LSMSA) method. The absolute and relative accuracy of the computed gravimetric geoid models was estimated on GNSS/levelling points. Results exhibit the accuracy improved by 1.1 cm or 20% in terms of standard deviation when airborne and terrestrial gravity data was used for geoid computation, compared to the geoid model computed only from terrestrial gravity data. Finally, the spectral analysis of surface gravity anomaly grids and geoid models was performed, which provided insights into specific wavelength bands in which airborne gravity data contributed and improved the power spectrum.

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“…Three methods for the determination of the least-squares parameters were summarized by Sjöberg, including biased least-squares modification (BLSM) [13], unbiased least-squares modification (ULSM) [14], and optimum least-squares modification (OLSM) [15]. The KTH method maximizes the advantage of the gravity field model in medium and long-wavelength sub-items, reduces the truncation error caused by the maximum order of the model, and has produced high accuracy geoid models in many countries, such as Sweden [16], Baltic countries [17,18], Iran [19], Tanzania [20], Sudan [21], Turkey [22], and Uganda [23].…”

Section: Introductionmentioning

confidence: 99%

“…The results of the two methods produced large differences in the high mountainous areas that occupied a few portions of the study area [30]. Abbak selected the mountainous area of central Turkey as the experimental area with sparse terrestrial gravity data and a single GPS leveling route; the results showed that the KTH method was significantly better than the RCR method [22]. Abdalla conducted the same experiment in the Khartoum state of Sudan with the blank boundary of the area filled by EGM2008; the final results were produced at the decimeter level [31], which may be attributed to the poor accuracy of the original EGM2008 model in the area.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Geoid Refinement between XGM2016 and EGM2008 Based on the KTH and RCR Methods: Jilin Province, China

Wang

et al. 2020

Remote Sensing

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The selection of an appropriate global gravity field model and refinement method can effectively improve the accuracy of the refined regional geoid model in a certain research area. We analyzed the accuracy of Experimental Geopotential Model (XGM2016) based on the GPS-leveling data and the modification parameters of the global mean square errors in the KTH geoid refinement in Jilin Province, China. The regional geoid was refined based on Earth Gravitational Model (EGM2008) and XGM2016 using both the Helmert condensation method with an RCR procedure and the KTH method. A comparison of the original two gravity field models to the GPS-leveling benchmarks showed that the accuracies of XGM2016 and EGM2008 in the plain area of Jilin Province are similar with a standard deviation (STD) of 5.8 cm, whereas the accuracy of EGM2008 in the high mountainous area is 1.4 cm better than that of XGM2016, which may be attributed to its low resolution. The modification parameters between the two gravity field models showed that the coefficient error of XGM2016 model was lower than that of EGM2008 for the same degree of expansion. The different modification limits and integral radii may produce a centimeter level difference in global mean square error, while the influence of the truncation error caused by the integral was at the millimeter level. The terrestrial gravity data error accounted for the majority of the global mean square error. The optimal least squares modification obtained the minimum global mean square error, and the global mean square error calculated based on XGM2016 model was reduced by about 1~3 cm compared with EGM2008. The refined geoid based on the two gravity field models indicated that both KTH and RCR method can effectively improve the STD of the geoid model from about six to three centimeters. The refined accuracy of EGM2008 model using RCR and KTH methods is slightly better than that of XGM2016 model in the plain and high mountain areas after seven-parameter fitting. EGM2008 based on the KTH method was the most precise at ± 2.0 cm in the plain area and ± 2.4 cm in the mountainous area. Generally, for the refined geoid based on the two Earth gravity models, KTH produced results similar to RCR in the plain area, and had relatively better performance for the mountainous area where terrestrial gravity data is sparse and unevenly distributed.

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Comparison of the KTH and remove–compute–restore techniques to geoid modelling in a mountainous area

Cited by 24 publications

References 22 publications

Assessment of Global Geopotential Models for Modelling Malaysia Marine Geoid

Assessment of Global Geopotential Models for Modelling Malaysia Marine Geoid

Contribution of GRAV-D airborne gravity to improvement of regional gravimetric geoid modelling in Colorado, USA

Performance Comparison of Geoid Refinement between XGM2016 and EGM2008 Based on the KTH and RCR Methods: Jilin Province, China

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