Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.