Constrained Gravity Geometric Inversion with Sparse Low-Uncertainty Data

Geological boundary parametrization of the subsurface that is consistent with geological observation is the goal of 3D geological modelling. Several approaches have been developed for geometric inversion and joint inversion of geophysical data sets. Recovering the geological boundary of units using the level-set gravity inversion method has been studied in recent years with the focus on inversion for different numbers and shapes of the buried bodies.

We choose to focus on constraining 3D gravity inversion with sparse available 2D low-uncertainty data within the modelling area which allows accounting for the uncertainty of the sparse data.

A level-set approach which is not limited to the number and shape of geological units is used for gravity inversion of Yamarna terrane in the Yilgarn craton, Western Australia. The study focuses on the eastern zone of the area which is mostly composed of greenstones and metagranitic rocks. As for mineral exploration, targets are small-scale, thus high-resolution models are required for exploration purposes. However, high-resolution data are sparsely distributed within the study area. In this study, interpreted sections from seismic data, have been used for constraining the surface evolution of rock unit boundaries during the gravity inversion.

The proposed work is the first we know of that implements a level-set inversion method for data sets with different spatial coverage. Our results indicate that unit boundaries from gravity inversion can be very well constrained when lower-uncertainty data are available. With this approach, to avoid bias along with the interpretation, uncertainty of the sparse data is also taken into account showing that the final model is consistent with all available data sets.

Our finding is a step toward building a 3D geological model which is consistent with available geological and geophysical data sets. As uncertainty plays an important role in geoscientific data and modelling, geometry inversion of gravity data in this study accounts for uncertainty meaning that sparse data are allowed to vary during the inversion within the threshold limit.

The proposed method has resulted in a reduction in ill-posedness of the inversion problem by creating a model with consistency to all high-resolution interpreted sections sparsely distributed within the model.