Harmonization of marine gravity data in Eastern Baltic

Abstract

Marine gravity datasets covering areas of state scale typically are made up of data that has been surveyed over multiple campaigns. These campaigns often take place many years apart, are of varying resolution and accuracy. This is due to ship-borne campaigns being much more expensive and time consuming than the ones on land; because of this, there is added value in validating and possibly correcting older data sets.

Over the course of recent BalMarGrav marine gravity project, dense, high quality gravity data was obtained covering most of the Latvian exclusive economic zone. The new data have been compared to campaigns, both as means of new data validation, and to check for possible biases among older data sets. Purpose of this research is to further the effort, to provide wider coverage of old marine gravity points, to test automated gravity point digitization, and to perform inter-campaign comparisons, using new, filtered and more precise data.

Data recovered during this research covers the previous data gaps between sets used in previous research. Using a more complete data coverage can improve new campaign data set validation and provide insights on inter-campaign biases within older data. Recovered data cover shallow coastal areas, where gravity mapping was not done over BalMarGrav project. Thus, by applying correctional values geoid errors can be minimized in the problematic transition zone between terrestrial and marine data.

Survey reports containing 20th century marine gravity point data were digitized, using optical character recognition. Gravity point values were transferred to sea surface and transformed to modern reference frames. Both modern and historic marine gravity data were filtered for gross errors and bias tracks. Data set robustness was checked, using leave-one-out cross validation. After processing, a comparison was made between old and new data.

Results present re-processed and filtered marine gravity datasets, and their comparison statistics. Comparison statistics before and after filtering reveal the increased accuracy and precision of filtered data. Mean comparison values reveal inter-campaign biases and provide correction values, which can increase data accuracy for use as input in future research and surface modelling.