North Atlantic Water in the Barents Sea Opening, 1997 to 1999

North Atlantic Water (NAW) is an important source of heat and salt to the Nordic seas and the Arctic Ocean. To measure the transport and variability of one branch of NAW entering the Arctic, a transect across the entrance to the Barents Sea was occupied 13 times between July 1997 and November 1999, and hydrography and currents were measured. There is large variability between the cruises, but the mean currents and the hydrography show that the main inflow takes place in Bjørnøyrenna, with a transport of 1.6 Sv of NAW into the Barents Sea. Combining the flow field with measurements of temperature and salinity, this results in mean heat and salt transports by NAW into the Barents Sea of 3.9×10¹³ W and 5.7×10⁷ kg s⁻¹, respectively. The NAW core increased in temperature and salinity by 0.7 °C yr⁻¹ and 0.04 yr⁻¹, respectively, over the observation period. Variations in the transports of heat and salt are, however, dominated by the flow field, which did not exhibit a significant change.     

Artificial neural networks for removal of couplings in airborne transient electromagnetic data

Modern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man‐made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist. Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well‐trained network can produce high‐quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%.     

Grain boundary energy as a clue to mineral segregations — A preliminary study

Dihedral angles between two grains of quartz and one of pyrrhotite, sphalerite resp. hematite are measured at triple junctions. An approach to equilibrium is noted. The sulphides have dihedral angles greater than 120 ° and the oxide less than 120 °. No significant influence of sphalerite composition on dihedral angle can be proved. It is concluded that the average grain boundary energy between two grains of quartz is smaller than that between quartz and a sulphide but greater than that between quartz and hematite. Sulphide — biotite interfaces are found to be parallel to the biotite cleavage. Parallelism is concluded to meet a requirement of minimum grain boundary energy. Assuming random distribution of grains, the interfacial area between sulphides and transparent minerals is found to be too small in comparison with that between sulphides. The sulphide clustering, observed and inferred, is suggested to depend on a combination of grain boundary energy and epitaxial growth, but may to some extent be inherited from the ore-generation stage.     

Modeling and visualizing uncertainties of flood boundary delineation: algorithm for slope and DEM resolution dependencies of 1D hydraulic models

As flood inundation risk maps have become a central piece of information for both urban and risk management planning, also a need to assess the accuracies and uncertainties of these maps has emerged. Most maps show the inundation boundaries as crisp lines on visually appealing maps, whereby many planners and decision makers, among others, automatically believe the boundaries are both accurate and reliable. However, as this study shows, probably all such maps, even those that are based on high-resolution digital elevation models (DEMs), have immanent uncertainties which can be directly related to both DEM resolution and the steepness of terrain slopes perpendicular to the river flow direction. Based on a number of degenerated DEMs, covering areas along the Eskilstuna River, Sweden, these uncertainties have been quantified into an empirically-derived disparity distance equation, yielding values of distance between true and modeled inundation boundary location. Using the inundation polygon, the DEM, a value representing the DEM resolution, and the desired level of confidence as inputs in a new-developed algorithm that utilizes the disparity distance equation, the slope and DEM dependent uncertainties can be directly visualized on a map. The implications of this strategy should benefit planning and help reduce high costs of floods where infrastructure, etc., have been placed in flood-prone areas without enough consideration of map uncertainties.     

The computation of gravity at elevation on a large scale

This paper briefly describes the techniques and computer programs being used to compute gravity at points located at aircraft elevations. Methods of mean anomaly prediction, the upward continuation of the gravity anomalies, and finally, the method used for normal gravity computations are outlined.