A comparison of filtering techniques for airborne gravimetry
Date
1996
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Abstract
Three different filtering techniques for the estimation of the gravity disturbance are theoretically outlined and numerically tested. The first approach, finite impulse response lowpass filtering, is a frequency domain method. The two other approaches, Kalman filtering and deterministic model filtering, are model based methods. An airborne gravity data set is used to compare these methods numerically. This comparison is based on an error measure, the root mean square error between the estimate and the reference. The reference is obtained by upward continuating a grid of surface free-air gravity anomalies and a terrain model. The lowpass filter provided the best estimation accuracy of the gravity disturbance and works equally well for any type of area. The deterministic model filter is better than the Kalman filter for smooth areas but is very sensitive to rapid changes of the gravity field. Kalman filtering requires an accurate covariance function of the gravity field of the flown area, and is very sensitive to its uncertainties.
Description
Bibliography: p. 99-103.
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Citation
Hammada, Y. (1996). A comparison of filtering techniques for airborne gravimetry (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/22591