| Summary: | Gravimetry is a subarea of research within geophysics that provides insights into the variations in the gravitational field strength based on various geographical terrains. During gravimetric surveys, episodes of rainfall result in fluctuations in the measurements of g. This project aims to propose the role that precipitation plays in causing discrepancies in gravimetric measurements. The methodology involves the use of a recursive algorithm known as the Kalman Filter, which employs a Bayesian approach to estimating system parameters. Two variants of the Kalman filter are introduced: the linear and extended Kalman filters (LKF and EKF respectively), along with their respective equations corresponding to the algorithm's two major steps, prediction and correction. The models used in these filters are derived based on three different soil density regimes. The LKF and EKF are then utilized in the analysis of Δg, the difference between outdoor and indoor gravitational field strength. Based on this analysis, the superior filter and model that best accounts for rain is established.
|
|---|