ASCH or ERMapper format

No abstract available

Inverse modelling of gravity/ or magnetic data is an essential component in geoscience research. Various numerical techniques in solving inverse problem for potential field data have been evolved over many years. However, the non-uniqueness in inverse solution and uncertainty in model building exercise still remain elusive. It is widely believed that additional information as soft constraint may ease the situation. This encourages pondering about joint inversion of gravity and magnetic data. Unfortunately, it often turns out that the constraints of joint inversion of gravity and magnetic data no longer becomes soft and it becomes more difficult to obtain optimal solution that would honour both the data set, which are two mutually competitive members. To circumvent such problem we propose of choosing Pareto optimal solution from the solution space. Between two competitive members such solution guarantees to make one member better off without making other member worse off. We consider L2-norm measure of fit between observed and computed data. We use particle swarm optimization (PSO), a global optimization technique to minimize the misfit between observed and computed data. We determine the Pareto front and hence the Pareto optimal solution from the cluster solutions in the solution space. Applications of the method on synthetic and field cases are presented.

This grid represents gravity anomalies of the Australian region. The grid combines accurate onshore gravity measurements, a sub-sample of the levelled offshore marine gravity traverses with satellite data used in areas where there is no marine data. The cell values represent simple Bouguer anomalies at a density of 2.67 tm-3 onshore and free-air anomalies offshore. The grid cell size is 0.5 minutes of arc, which is equivalent to about 800 metres. The smallest wavelength contained in the grid is 1600 m.

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Legacy product - no abstract available