Localised Gross-error Detection in the Australian Land Gravity Database
We have used two complementary, data-driven gross-error detection methods to clean the 2004 release of Geoscience Australia?s (GA?s) land gravity database. The first uses the DEM-9S (version 2) Australian digital elevation model to help verify the gravity observation elevations stored in the databa...
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| Format: | Journal Article |
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RESoultions Resource & Energy Services Pty Ltd
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/14369 |
| _version_ | 1848748604694462464 |
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| author | Sproule, David Featherstone, Will Kirby, Jonathan |
| author_facet | Sproule, David Featherstone, Will Kirby, Jonathan |
| author_sort | Sproule, David |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We have used two complementary, data-driven gross-error detection methods to clean the 2004 release of Geoscience Australia?s (GA?s) land gravity database. The first uses the DEM-9S (version 2) Australian digital elevation model to help verify the gravity observation elevations stored in the database. The second method uses locally interpolated complete/refined Bouguer gravity anomalies, under the assumption that these are smooth and suitable for interpolation, to crosscheck each gravity observation against those surrounding. Together, these methods only identified a total of 237 points (0.021%) in the database that were suspected to be in gross error (differences greater than 250 m and 35 mgal, respectively), of which only nine were identified by both methods. These points will be removed before the computation of the new Australian geoid model, and also supplied to GA for its evaluation. Due to the small number of points identified, this is a very positive result in that it shows that the Australian gravity database appears relatively gross-error-free, which bodes well for all previous studies that have relied upon it. However, it is important to point out that this evaluation is inevitably localised and thus only verifies the high-frequency gravity anomaly signal content. Subsequent studies using dedicated satellite gravimetry will be used to identify long-wavelength errors. |
| first_indexed | 2025-11-14T07:07:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-14369 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:07:41Z |
| publishDate | 2006 |
| publisher | RESoultions Resource & Energy Services Pty Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-143692017-09-13T16:01:59Z Localised Gross-error Detection in the Australian Land Gravity Database Sproule, David Featherstone, Will Kirby, Jonathan gravity anomalies digital elevation model gross error detection data cleaning Gravity databases We have used two complementary, data-driven gross-error detection methods to clean the 2004 release of Geoscience Australia?s (GA?s) land gravity database. The first uses the DEM-9S (version 2) Australian digital elevation model to help verify the gravity observation elevations stored in the database. The second method uses locally interpolated complete/refined Bouguer gravity anomalies, under the assumption that these are smooth and suitable for interpolation, to crosscheck each gravity observation against those surrounding. Together, these methods only identified a total of 237 points (0.021%) in the database that were suspected to be in gross error (differences greater than 250 m and 35 mgal, respectively), of which only nine were identified by both methods. These points will be removed before the computation of the new Australian geoid model, and also supplied to GA for its evaluation. Due to the small number of points identified, this is a very positive result in that it shows that the Australian gravity database appears relatively gross-error-free, which bodes well for all previous studies that have relied upon it. However, it is important to point out that this evaluation is inevitably localised and thus only verifies the high-frequency gravity anomaly signal content. Subsequent studies using dedicated satellite gravimetry will be used to identify long-wavelength errors. 2006 Journal Article http://hdl.handle.net/20.500.11937/14369 10.1071/EG06175 RESoultions Resource & Energy Services Pty Ltd fulltext |
| spellingShingle | gravity anomalies digital elevation model gross error detection data cleaning Gravity databases Sproule, David Featherstone, Will Kirby, Jonathan Localised Gross-error Detection in the Australian Land Gravity Database |
| title | Localised Gross-error Detection in the Australian Land Gravity Database |
| title_full | Localised Gross-error Detection in the Australian Land Gravity Database |
| title_fullStr | Localised Gross-error Detection in the Australian Land Gravity Database |
| title_full_unstemmed | Localised Gross-error Detection in the Australian Land Gravity Database |
| title_short | Localised Gross-error Detection in the Australian Land Gravity Database |
| title_sort | localised gross-error detection in the australian land gravity database |
| topic | gravity anomalies digital elevation model gross error detection data cleaning Gravity databases |
| url | http://hdl.handle.net/20.500.11937/14369 |