This article reviews a spectral forward gravity field modelling method that was initially designed for topographic/isostatic mass reduction of gravity data. The method transforms 3D spherical density models into gravitational potential fields using a spherical harmonic representation. The binomial series approximation in the approach, which is crucial for its computational efficiency, is examined and an error analysis is performed. It is shown that, this method cannot be used for density layers in crustal and upper mantle regions, because it results in large errors in the modelled potential field. Here, a correction is proposed to mitigate this erroneous behaviour. The improved method is benchmarked with a tesseroid gravity field modelling method and is shown to be accurate within ±4 mGal for a layer representing the Moho density interface, which is below other errors in gravity field studies. After the proposed adjustment the method can be used for the global gravity modelling of the complete Earth's density structure.

The Metis research project aims at supporting maritime safety and security by facilitating continuous monitoring of vessels in national coastal waters and prevention of phenomena, such as vessel collisions, environmental hazard, or detection of malicious intents, such as smuggling. Surveillance systems such as Metis typically comprise a number of heterogeneous information sources and information aggregators. Among the main problems of their deployment lies their scalability with respect to a potentially large number of monitored entities. One of the solutions to the problem is continuous and timely adaptation and reconfiguration of the system according to the changing environment it operates in. At any given timepoint, the system should use only a minimal set of information sources and aggregators needed to facilitate effective and early detection of indicators of interest. Here, we describe the Metis system prototype and introduce a theoretical framework for modelling scalable information-aggregation systems. We model information-aggregation systems as networks of inter-dependent reasoning agents, each representing a mechanism for justification/refutation of a conclusion derived by the agent. The proposed continuous reconfiguration algorithm relies on standard results from abstract argumentation and corresponds to computation of a grounded extension of the argumentation framework associated with the system. Finally, we demonstrate the flexibility of the presented framework by extending the proposed algorithm to adapt to context-dependent changes in information sources availability, as well as shifts in system's focus according to its context.