Legacy product - no abstract available

Legacy product - no abstract available

Legacy product - no abstract available

Legacy product - no abstract available

Legacy product - no abstract available

Gravity and magnetic modelling with GeoModeller

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.

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No abstract available