The regional gravity field modeling by means of classical remove-compute-restore procedures is nowadays widely used in different contexts: from geodetic applications for the regional gravimetric geoid determination to exploration geophysics applications to extrapolate gridded or sparse points values of gravity anomalies (Bouguer, free-air, isostatic, etc.), useful to understand and map geological structures in a specific region. However, the accuracies and resolutions required for such exploration activity, do not consent the exploitation of satellite only gravity field data but need the integration with observation acquired at lower altitude. Thanks to the development, in the late eighties and early nineties, of Global Navigation Satellite Systems (GNSS) and the consequent availability of accurate navigational data, techniques such as airborne gravimetry, that can provide this complementary information, started to spread worldwide. This technique represents nowadays one of the most efficient techniques ideal to collect gravity observations close to the Earth’s surface, in a fast and cost-effective way. Airborne gravimetry is capable of providing gravity measurements also in challenging environments which can be difficult to access otherwise, such as mountainous areas, rain forests and polar regions. However due to the relatively high acquisition velocity, the presence of atmospheric turbulence, aircraft vibration, instrumental drift, etc. airborne data are usually contaminated by a very high observation error. For this reason a proper procedure to filter the raw observations both in the low and high frequency should be applied to recover valuable information. In this work a new methodology to process airborne gravity measurements, named Very Improved KINematic Gravimetry (VIKING) is presented. The proposed procedure allows to pre-process the raw observations coming from both the GNSS receiver and the gravimeter, with the aim to optimally combine the derived accelerations to compute gravity disturbances. Furthermore it consents to process by means of a filtering and gridding procedure these latter raw gravity disturbances to predict the signal on other points (grids or sparse points). In details, the pre-processing deals with the manipulation of data acquired from the on board gravimeter and GNSS receiver to correct biases and derive gravity accelerations; while the processing regards the procedure to filter and grid the gravity accelerations data to obtain gravity anomalies/disturbances maps. The developed algorithms used to pre-process raw GNSS acquired data are basically obtained by manipulating the classical GNSS observation equation to derive a new expression sensitive to the receiver acceleration (which corresponds to the vehicle acceleration) but almost insensitive to its actual position, by means of the implementation of the variometric approach. Regarding the pre-processing of the gravimeter data, the two principal aims of the method are the computation of all the corrections to properly combine gravimeter observations with GNSS observations and the optimal sampling of the gravimeter data, characterized by a very high observation rate, in such a way to not have loss of valuable information in terms of gravity accelerations. The proposed solution to filter and grid raw airborne observations is a remove-compute-restore like procedure, and consists in a combination of an along track Wiener filter and a classical Least Squares Collocation technique. Basically the proposed procedure is an adaptation to airborne gravimetry of the Space-Wise approach, developed by Politecnico di Milano, to process data coming from the ESA satellite mission GOCE. Among the main differences with respect to the satellite application of this approach there is the fact that, while in processing GOCE data the stochastic characteristics of the observation error can be considered a-priori well known, in airborne gravimetry, due to the complex environment in which the observations are acquired, these characteristics are unknown and should be retrieved from the dataset itself. The presented VIKING methodology is suited for airborne data analysis in order to be able to quickly filter and grid gravity observations in an easy fast and accurate way. Some innovative theoretical aspects focusing in particular on the theoretical covariance modeling are presented too. An important part of the whole research project regarded the implementation of a suitable software for airborne gravity data processing. It has been developed in parallel C language and is organized in a set of toolboxes, which can be run independently from all the other ones, or in sequence to perform the whole processing. In order to evaluate the goodness of the whole procedure and its performances, various numerical tests have been performed on a real aerogravimetric dataset. The different tests were mainly focused on the analysis of the optimal choice of some parameters involved in the computation and on the performances in terms of accuracy and computational times of the various modules. The final result of the whole VIKING procedure, once calibrated the different parameters accordingly to the numerical tests performed, shows a predicted signal with ac- curacies of about 1.3 mGal. The obtained result in term of accuracy is in line with the expectations derived from the specific survey characteristics.
|Titolo:||Very improved KINematic gravimetry: a new approach to aerogravimetry|
|Data di discussione:||21-feb-2018|
|Appartiene alla tipologia:||07a Tesi di Dottorato|