Crustal deformation monitoring by the Kalman filter method
The Earth's crust is deforming continuously due to plate tectonics. Deformation at plate boundaries causes volcanoes and most destructive earthquakes. Monitoring such deformation is essential to gaining an insight into the mechanisms of plate tectonics. Deformation analysis is one of the most...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
1998
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| Online Access: | https://eprints.nottingham.ac.uk/11438/ |
| _version_ | 1848791276515753984 |
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| author | Çelik, Cahit Tăgi |
| author_facet | Çelik, Cahit Tăgi |
| author_sort | Çelik, Cahit Tăgi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The Earth's crust is deforming continuously due to plate tectonics. Deformation at plate boundaries causes volcanoes and most destructive earthquakes. Monitoring such deformation is essential to gaining an insight into the mechanisms of plate tectonics.
Deformation analysis is one of the most important aspects of geodetic research. Space geodesy, with which long baselines can be measured to millimetre accuracy, plays an important role in determining crustal deformation parameters, since deformation in general, means a change in geometric configuration. The main deformation monitoring problem is to determine the spatial relationship of a set of object points relative to a number of reference points. Ideally reference and object point observations are made at regular intervals. After mathematical adjustment of each epoch's observations, which includes 'data snooping', a displacement vector is obtained by simply differencing the estimated coordinates at consecutive epochs. The use of thismethod also however, increases the noise level.
In this thesis, the author proposes a deformation analysis technique which mainly uses a Kalman Filter. However, Kalman filter estimation may not be optimal if local movement occurs between observation epochs. To overcome this kind of deficiency, two sub-optimal filters have been proposed: Fading Memory Filter and Adaptive Kalman Filter for a System with Unknown Measurement Bias. These two filtering techniques have been used in this research and tested on real/simulated data based on the EASTMED project. In addition to this, data from the EUREF Permanent GPS Network, and from the UK Tide Gauge Monitoring C Project are also processed and the result presented. |
| first_indexed | 2025-11-14T18:25:56Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-11438 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:25:56Z |
| publishDate | 1998 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-114382025-02-28T11:13:24Z https://eprints.nottingham.ac.uk/11438/ Crustal deformation monitoring by the Kalman filter method Çelik, Cahit Tăgi The Earth's crust is deforming continuously due to plate tectonics. Deformation at plate boundaries causes volcanoes and most destructive earthquakes. Monitoring such deformation is essential to gaining an insight into the mechanisms of plate tectonics. Deformation analysis is one of the most important aspects of geodetic research. Space geodesy, with which long baselines can be measured to millimetre accuracy, plays an important role in determining crustal deformation parameters, since deformation in general, means a change in geometric configuration. The main deformation monitoring problem is to determine the spatial relationship of a set of object points relative to a number of reference points. Ideally reference and object point observations are made at regular intervals. After mathematical adjustment of each epoch's observations, which includes 'data snooping', a displacement vector is obtained by simply differencing the estimated coordinates at consecutive epochs. The use of thismethod also however, increases the noise level. In this thesis, the author proposes a deformation analysis technique which mainly uses a Kalman Filter. However, Kalman filter estimation may not be optimal if local movement occurs between observation epochs. To overcome this kind of deficiency, two sub-optimal filters have been proposed: Fading Memory Filter and Adaptive Kalman Filter for a System with Unknown Measurement Bias. These two filtering techniques have been used in this research and tested on real/simulated data based on the EASTMED project. In addition to this, data from the EUREF Permanent GPS Network, and from the UK Tide Gauge Monitoring C Project are also processed and the result presented. 1998 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/11438/1/263414.pdf Çelik, Cahit Tăgi (1998) Crustal deformation monitoring by the Kalman filter method. PhD thesis, University of Nottingham. |
| spellingShingle | Çelik, Cahit Tăgi Crustal deformation monitoring by the Kalman filter method |
| title | Crustal deformation monitoring by the Kalman filter method |
| title_full | Crustal deformation monitoring by the Kalman filter method |
| title_fullStr | Crustal deformation monitoring by the Kalman filter method |
| title_full_unstemmed | Crustal deformation monitoring by the Kalman filter method |
| title_short | Crustal deformation monitoring by the Kalman filter method |
| title_sort | crustal deformation monitoring by the kalman filter method |
| url | https://eprints.nottingham.ac.uk/11438/ |