Harmonisation of marine gravity data in the Eastern Baltic

Abstract

Marine gravity datasets covering areas of state scale typically consist of data surveyed over multiple campaigns. These campaigns often take place many years apart and are of varying resolution and accuracy. Ship-borne campaigns are 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. Throughout the recent BalMarGrav marine gravity project, dense, high-quality gravity data covered most of the Latvian exclusive economic zone. The new data have been compared to campaigns, both as a means of new data validation and to check for possible biases among older data sets. This research aims to further the effort, provide more exhaustive coverage of old marine gravity points, test automated gravity point digitisation, and 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. The recovered data cover shallow coastal areas, where gravity mapping was not done over the BalMarGrav project. Thus, by applying correctional values, geoid errors can be minimised in the problematic transition zone between terrestrial and marine data. Survey reports containing 20th-century marine gravity point data were digitised using optical character recognition. Gravity point values were transferred to the sea surface and transformed to modern reference frames. 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 data’s increased accuracy and precision. Mean comparison values reveal intercampaign biases and provide correction values, which can increase data accuracy for use as input in future research and surface modelling.