Outlier detection algorithms and their performance in GOCE gravity field processing
Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps h...
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| Autor*in: |
Kern, M. [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of geodesy - Springer-Verlag, 1995, 78(2005), 9 vom: 29. Jan., Seite 509-519 |
|---|---|
| Übergeordnetes Werk: |
volume:78 ; year:2005 ; number:9 ; day:29 ; month:01 ; pages:509-519 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00190-004-0419-9 |
|---|
| Katalog-ID: |
OLC2058938410 |
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| 520 | |a Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. | ||
| 650 | 4 | |a Outlier detection | |
| 650 | 4 | |a Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission | |
| 650 | 4 | |a Satellite gravity gradiometry | |
| 650 | 4 | |a Quick-Look Gravity Field Analysis | |
| 700 | 1 | |a Preimesberger, T. |4 aut | |
| 700 | 1 | |a Allesch, M. |4 aut | |
| 700 | 1 | |a Pail, R. |4 aut | |
| 700 | 1 | |a Bouman, J. |4 aut | |
| 700 | 1 | |a Koop, R. |4 aut | |
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10.1007/s00190-004-0419-9 doi (DE-627)OLC2058938410 (DE-He213)s00190-004-0419-9-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kern, M. verfasserin aut Outlier detection algorithms and their performance in GOCE gravity field processing 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2005 Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. Outlier detection Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission Satellite gravity gradiometry Quick-Look Gravity Field Analysis Preimesberger, T. aut Allesch, M. aut Pail, R. aut Bouman, J. aut Koop, R. aut Enthalten in Journal of geodesy Springer-Verlag, 1995 78(2005), 9 vom: 29. Jan., Seite 509-519 (DE-627)191686298 (DE-600)1302972-1 (DE-576)051377373 0949-7714 nnns volume:78 year:2005 number:9 day:29 month:01 pages:509-519 https://doi.org/10.1007/s00190-004-0419-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4700 AR 78 2005 9 29 01 509-519 |
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10.1007/s00190-004-0419-9 doi (DE-627)OLC2058938410 (DE-He213)s00190-004-0419-9-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kern, M. verfasserin aut Outlier detection algorithms and their performance in GOCE gravity field processing 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2005 Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. Outlier detection Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission Satellite gravity gradiometry Quick-Look Gravity Field Analysis Preimesberger, T. aut Allesch, M. aut Pail, R. aut Bouman, J. aut Koop, R. aut Enthalten in Journal of geodesy Springer-Verlag, 1995 78(2005), 9 vom: 29. Jan., Seite 509-519 (DE-627)191686298 (DE-600)1302972-1 (DE-576)051377373 0949-7714 nnns volume:78 year:2005 number:9 day:29 month:01 pages:509-519 https://doi.org/10.1007/s00190-004-0419-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4700 AR 78 2005 9 29 01 509-519 |
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10.1007/s00190-004-0419-9 doi (DE-627)OLC2058938410 (DE-He213)s00190-004-0419-9-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kern, M. verfasserin aut Outlier detection algorithms and their performance in GOCE gravity field processing 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2005 Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. Outlier detection Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission Satellite gravity gradiometry Quick-Look Gravity Field Analysis Preimesberger, T. aut Allesch, M. aut Pail, R. aut Bouman, J. aut Koop, R. aut Enthalten in Journal of geodesy Springer-Verlag, 1995 78(2005), 9 vom: 29. Jan., Seite 509-519 (DE-627)191686298 (DE-600)1302972-1 (DE-576)051377373 0949-7714 nnns volume:78 year:2005 number:9 day:29 month:01 pages:509-519 https://doi.org/10.1007/s00190-004-0419-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4700 AR 78 2005 9 29 01 509-519 |
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10.1007/s00190-004-0419-9 doi (DE-627)OLC2058938410 (DE-He213)s00190-004-0419-9-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kern, M. verfasserin aut Outlier detection algorithms and their performance in GOCE gravity field processing 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2005 Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. Outlier detection Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission Satellite gravity gradiometry Quick-Look Gravity Field Analysis Preimesberger, T. aut Allesch, M. aut Pail, R. aut Bouman, J. aut Koop, R. aut Enthalten in Journal of geodesy Springer-Verlag, 1995 78(2005), 9 vom: 29. Jan., Seite 509-519 (DE-627)191686298 (DE-600)1302972-1 (DE-576)051377373 0949-7714 nnns volume:78 year:2005 number:9 day:29 month:01 pages:509-519 https://doi.org/10.1007/s00190-004-0419-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4700 AR 78 2005 9 29 01 509-519 |
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10.1007/s00190-004-0419-9 doi (DE-627)OLC2058938410 (DE-He213)s00190-004-0419-9-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Kern, M. verfasserin aut Outlier detection algorithms and their performance in GOCE gravity field processing 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2005 Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. Outlier detection Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission Satellite gravity gradiometry Quick-Look Gravity Field Analysis Preimesberger, T. aut Allesch, M. aut Pail, R. aut Bouman, J. aut Koop, R. aut Enthalten in Journal of geodesy Springer-Verlag, 1995 78(2005), 9 vom: 29. Jan., Seite 509-519 (DE-627)191686298 (DE-600)1302972-1 (DE-576)051377373 0949-7714 nnns volume:78 year:2005 number:9 day:29 month:01 pages:509-519 https://doi.org/10.1007/s00190-004-0419-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4700 AR 78 2005 9 29 01 509-519 |
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Outlier detection algorithms and their performance in GOCE gravity field processing |
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Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. © Springer-Verlag Berlin Heidelberg 2005 |
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Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. © Springer-Verlag Berlin Heidelberg 2005 |
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Abstract. The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data. © Springer-Verlag Berlin Heidelberg 2005 |
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