Pluri-Gaussian rapid updating of geological domains

TL;DR

This paper introduces a rapid updating methodology for geological domains using pluri-Gaussian simulation, integrating real-time sensor data to improve resource models by jointly updating categorical and continuous variables.

Contribution

It extends data assimilation techniques to categorical geological variables, combining Gibbs sampling, ensemble Kalman filtering, and Gaussianisation for near-real-time updates.

Findings

Improved accuracy of geological domain geometries.

Closer match between model predictions and observations.

Enhanced resource model realism.

Abstract

Over the past decade, the rapid updating of resource knowledge and the integration of real-time sensor information have gained attention in both industry and academia. However, most studies on rapid resource model updating have focused on continuous variables, such as grade variables and coal quality parameters. Geological domain modelling is an essential component of resource estimation, which is why it is crucial to extend data assimilation techniques to enable the rapid updating of categorical variables. In this paper, a methodology inspired by pluri-Gaussian simulation is proposed for near-real-time updating of geological domains, followed by updating grade variables within these domain boundaries. The proposed algorithm consists of a Gibbs sampler for converting geological domains into Gaussian random fields, an ensemble Kalman filter with multiple data assimilations for rapid updating, and rotation based iterative Gaussianisation for multi-Gaussian transformation. We demonstrate the algorithm by using a synthetic case study with observations sampled from the ground truth, as well as a real case study that uses production drilling samples to jointly update geological domains and grade variables. Both case studies are based on real data from an iron oxide-copper-gold deposit in South Australia. This approach enhances resource knowledge by incorporating both categorical and continuous variables, leading to improved reproduction of domain geometries, closer matches between predictions and observations, and more geologically realistic resource models.