Meteorites contain information on the formation and evolution of the Solar System. Antarctica is the most productive region for collecting meteorites, as the visually contrasting meteorites are easily detectable and tend to concentrate at specific areas exposing blue ice. Blue ice areas act as meteorite stranding surfaces (MSSs) if the flow of the ice sheet and specific geographical and climatological settings combine favorably. Previously, possible meteorite stranding surfaces were identified by chance or through visual examination of remote sensing data, which have limitations in discovering new locations for future meteorite searching campaigns. In this study, datasets are combined in a novel machine learning approach in order to estimate the likeliness of a blue ice area to be a meteorite stranding surface. Input data consists of positive and unlabeled observations. The ca. 2,500 positive observations are defined as the centers of regularly spaced grid cells containing one or more meteorite finds. The ca. 2,000,000 unlabeled observations, for which the presence of meteorites is unknown, are defined as the centers of regularly spaced grid cells overlaying blue ice areas. The size of a grid cell is 450 by 450 meter. Features of the observations, such as the surface velocity, the surface temperature, and the ice thickness, are extracted from geospatial datasets. Individual features and correlations between features indicate that positive observations differ from unlabeled observations.

The unlabeled observations are classified as MSS or non-MSS by training a classifier with the nontraditional training set consisting of positive and unlabeled data. The obtained classification is validated and evaluated quantitatively with positive and negative observations, where the latter are defined after investigating fieldwork reports. With an estimated accuracy of 80%, the classification shows promising results. The influence of the different features on the classification does confirm the current, qualitative, understanding of the meteorite concentration mechanism and provides a quantification of how individual features affect the meteorite concentration. In the visualization of the as MSS-classified observations, the probabilistic character of the obtained results is considered by using a color scale ranging from yellow to red. These colors indicate how likely it is to find meteorites at a MSS-classified observation (i.e. the precision of the classification). This leaves the interpretation of the obtained meteorite hotspot map to the user.

Measurements of a tachymetric network in the geothermal area of Bjarnarflag in North-East Iceland are performed during four consecutive years (2015-2018). For the yearly adjustment of the measurements an alternative iteration scheme to Baarda’s ’B-method of testing’ is proposed, because the level of significance of the F-test performed in the detection step is too large due to the large redundancy of the measurements. The proposed alternative method detects outliers and verifies the stochastic model simultaneously, resulting in 3
to 4% rejected measurements per year. From the yearly solutions absolute and relative horizontal velocities are estimated. The relative velocities are significant, but have large uncertainties. Adding three years of data reduces the mean standard deviation of the estimated velocities from 2.4 mm in east-direction to 0.9 mm and 1.8 mm in north-direction to 0.8 mm. The improvement of precision can be accelerated by reevaluating the stochastic input parameters, adding more GNSS measurements and reconsidering the network design.
Improved horizontal relative velocities can be used to understand the horizontal deformation patterns in the area of study due to extraction of water or steam by the geothermal powerplant, due to the instability of the benchmaks or due to natural processes.