TREND MODELS IN THE LEAST-SQUARES PREDICTION OF FREE-AIR GRAVITY ANOMALIES

Abstract

The different wavelength components of the anomalous gravity field were treated as trend, signal and noise parts of this field. Since signal models have already been investigated extensively by others, therefore the role of deterministic information used in the predic- tion process was emphasized. In order to improve the reliability of prediction, several trend models were tested on regional and local data sets in the Pannonian Basin. The results show that the prediction errors can be significantly reduced by applying simple and generalized physical trend models, although it is a laboursome task to produce high quality prediction below ±1 mgal R.M.S., even if the data point density is high (e.g. 1 point/km2 ) Since the method of Least-Squares Prediction is not an automatic process (that is its use requires an a priori analysis of the physical-statistical content of input data), beyond the practical results useful information can be gained from the solution to a prediction problem about the features of the gravity field.