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Assessment of local covariance estimation through Least Squares Collocation over Iran. / Ramouz, Sabah ; Afrasteh, Yosra; Reguzzoni, Mirko ; Safari, Abdolreza.

In: Advances in Geosciences, Vol. 50, 2020, p. 65-75.

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Ramouz, Sabah ; Afrasteh, Yosra ; Reguzzoni, Mirko ; Safari, Abdolreza. / Assessment of local covariance estimation through Least Squares Collocation over Iran. In: Advances in Geosciences. 2020 ; Vol. 50. pp. 65-75.

BibTeX

@article{0cbb0b1035ae4b2d9435634dd0aca58a,
title = "Assessment of local covariance estimation through Least Squares Collocation over Iran",
abstract = "Covariance determination as the heart of Least Squares Collocation gravity field modeling is based on fitting an analytical covariance to the empirical covariance, which is stemmed from gravimetric data. The main objective of this study is to process different local covariance strategies over four regions with different topography and spatial data distribution in Iran. For this purpose, Least Squares Collocation based on Remove-Compute-Restore technique is implemented. In the Remove step, gravity reduction in regions with a denser distribution and a rougher topography is more effective. In the Compute step, the assessment of the Collocation estimates on the gravity anomaly control points illustrates that data density is more relevant than topography roughness to have a good covariance determination. Moreover, among the different attempts of localizing the covariance estimation, a recursive approach correcting the covariance parameters based on the agreement between Least Squares Collocation estimates and control points shows better performance. Furthermore, we could see that covariance localization in a region with sparse or bad distributed observations is a challenging task and may not necessarily improve the Collocation gravity modeling. Indeed, the geometrical fitness of the empirical and analytical covariances-which is usually a qualitative test to verify the precision of the covariance determination-is not always an adequate criterion.",
author = "Sabah Ramouz and Yosra Afrasteh and Mirko Reguzzoni and Abdolreza Safari",
year = "2020",
doi = "10.5194/adgeo-50-65-2020",
language = "English",
volume = "50",
pages = "65--75",
journal = "Advances in Geosciences",
issn = "1680-7340",
publisher = "European Geosciences Union",

}

RIS

TY - JOUR

T1 - Assessment of local covariance estimation through Least Squares Collocation over Iran

AU - Ramouz, Sabah

AU - Afrasteh, Yosra

AU - Reguzzoni, Mirko

AU - Safari, Abdolreza

PY - 2020

Y1 - 2020

N2 - Covariance determination as the heart of Least Squares Collocation gravity field modeling is based on fitting an analytical covariance to the empirical covariance, which is stemmed from gravimetric data. The main objective of this study is to process different local covariance strategies over four regions with different topography and spatial data distribution in Iran. For this purpose, Least Squares Collocation based on Remove-Compute-Restore technique is implemented. In the Remove step, gravity reduction in regions with a denser distribution and a rougher topography is more effective. In the Compute step, the assessment of the Collocation estimates on the gravity anomaly control points illustrates that data density is more relevant than topography roughness to have a good covariance determination. Moreover, among the different attempts of localizing the covariance estimation, a recursive approach correcting the covariance parameters based on the agreement between Least Squares Collocation estimates and control points shows better performance. Furthermore, we could see that covariance localization in a region with sparse or bad distributed observations is a challenging task and may not necessarily improve the Collocation gravity modeling. Indeed, the geometrical fitness of the empirical and analytical covariances-which is usually a qualitative test to verify the precision of the covariance determination-is not always an adequate criterion.

AB - Covariance determination as the heart of Least Squares Collocation gravity field modeling is based on fitting an analytical covariance to the empirical covariance, which is stemmed from gravimetric data. The main objective of this study is to process different local covariance strategies over four regions with different topography and spatial data distribution in Iran. For this purpose, Least Squares Collocation based on Remove-Compute-Restore technique is implemented. In the Remove step, gravity reduction in regions with a denser distribution and a rougher topography is more effective. In the Compute step, the assessment of the Collocation estimates on the gravity anomaly control points illustrates that data density is more relevant than topography roughness to have a good covariance determination. Moreover, among the different attempts of localizing the covariance estimation, a recursive approach correcting the covariance parameters based on the agreement between Least Squares Collocation estimates and control points shows better performance. Furthermore, we could see that covariance localization in a region with sparse or bad distributed observations is a challenging task and may not necessarily improve the Collocation gravity modeling. Indeed, the geometrical fitness of the empirical and analytical covariances-which is usually a qualitative test to verify the precision of the covariance determination-is not always an adequate criterion.

UR - http://www.scopus.com/inward/record.url?scp=85081197138&partnerID=8YFLogxK

U2 - 10.5194/adgeo-50-65-2020

DO - 10.5194/adgeo-50-65-2020

M3 - Article

AN - SCOPUS:85081197138

VL - 50

SP - 65

EP - 75

JO - Advances in Geosciences

JF - Advances in Geosciences

SN - 1680-7340

ER -

ID: 71446975