A short user guide for UBC-GIF gravity and magnetic inversions

Abstract

Obtaining reliable predictions of the subsurface will provide a critical advantage for explorers seeking mineral deposits at depth and beneath cover. A common approach in achieving this goal is to use deterministic property-based inversion of potential field data to predict a 3D subsurface distribution of physical properties that explain measured gravity or magnetic data. The non-uniqueness of inversions of potential field data mandates careful and consistent parameterization of the problem to ensure realistic solutions. Including all prior geological knowledge as constraints on the inversion also helps ensure that the recovered predictions are consistent with both the geophysical data and the geological knowledge.

We review how potential field inversions are best applied for mineral exploration problems using the UBC-GIF inversion algorithms. We use examples to emphasise the importance of mesh design and applying appropriate data processing, and identify the approach for defining key parameters such as data uncertainty, potential field weighting functions, and numerical parameters that approximate prior geological knowledge of in situ trends, geometries and properties. Consistent application of these techniques will ensure the most reliable predictive physical property models for explorers.