Map Analytics: Deconstructing geological maps

Abstract:

The advent of digital geological maps has not in general been matched by an uptake of the analysis of the data contained within these maps. One exception is the realm of mineral prospectivity mapping, although in general the analysis consists of simple buffers and object density measures. The difficulty preparing input data for repeatable 3D geological models has created a demand for increased automation in the model building process. Where available, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map, even recognising that a map is just a model, with all the hidden biases this implies. The information stored in a map falls into three categories of geometric data: positional data such as the position of faults, intrusive and stratigraphic contacts; topological data, such as the age relationships of faults and stratigraphic units, and gradient data, such as the dips of contacts or faults. Our workflow combines all of these direct observations with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a 3D geological model. This work is being conducted within the larger Loop Consortium, in which algorithms are being developed that allow automatic deconstruction of a geological map to recover the necessary positional, topological and gradient data as inputs to different 3D geological modelling codes. This automation provides significant advantages: it significantly reduces the time to first prototype models; it clearly separates the primary data from the data reduction steps and conceptual constraints; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification and Value of Information studies. The open source codes described here are available from https://github.com/Loop3D/map2loop .

Acknowledgement

We acknowledge the support from the ARC-funded Loop: Enabling Stochastic 3D Geological Modelling consortia (LP170100985) and DECRA (DE190100431). The work has also been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government's Cooperative Research Centre Programme. This is MinEx CRC Document 2020/40.