Full tensor gravity (FTG) data are known for their high resolution but also for higher noise in its components due to the dynamic nature of the platform used for data acquisition. Although a review of the literature suggests steady increase in the success of gravity gradiometry, we still cannot take advantage of the full potential of the method, mostly because of the noise with the same amplitude and wavenumber characteristics as the signal that affects these data. Smoothing from common low pass filters removes small wavelength features and makes it difficult to detect structural features and other density variations of interest to exploration. In Kalman filtering the components of the FTG are continuously updated to calculate the best estimation of the state. The most important advantage of the Kalman filter is that it can be applied on gravity gradiometry components simultaneously. In addition, one can incorporate constraints. We use the Laplace’s equation that is the most meaningful constraint for potential field data to extract signal from noise and improve the detection and continuity of density variations. We apply the Kalman filter on the FTG data acquired by Bell Geospace over the Vinton salt dome in southwest Louisiana.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:ees_etds-1029 |
| Date | 01 January 2014 |
| Creators | Sepehrmanesh, Mahnaz |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations--Earth and Environmental Sciences |
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