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

Gravity surveys are used to measure small changes in the Earth's gravity field. These changes are due to density variations in the Earth's crust and can be used for a range of investigations such as studies of deep tectonic structures or finding caves in urban engineering studies. The physical property being measured, density variation, is the same in these investigations; it is just the survey parameters and precision required that differ. This manual serves to give a brief outline of the theory behind gravity surveying and discusses the considerations that need to be addressed when conducting gravity surveys. Gravity survey design and field techniques are discussed including both gravity and positioning equipment. Survey reduction and processing techniques are also discussed. This manual is not a definitive text on gravity surveying but rather a guide to techniques that have proved to be successful. As such, it is hoped it will be updated as techniques and instrumentation improve. Feedback or suggestions are welcome.

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.

The GEOPHYS_SURV database describes geophysical surveys (air, land, and marine), the datasets derived from those surveys, and the methods used for delivery of those datasets. The database includes metadata for all surveys conducted or managed by Geoscience Australia and its predecessor agencies, as well as data and surveys from State and Territory geological survey agencies.

No abstract available