Human-AI interaction: Machine learning-based geostatistical hybrid models

Intelligent methods for estimating mineral grades have been developed but some tasks cannot be completely automated through artificial intelligence. Human-in-the-loop (HITL) approaches are being increasingly utilized, where the strengths of both human expertise and artificial intelligence are combined to improve outcomes. This study integrates HITL models with machine learning (ML) based geostatistical hybrid modelling and ensembling approaches for mineral grade estimation and ore sorting. In the hybrid modelling approach, ML models such as an elliptical radial basis neural network (ERBFN), locally weighted support vector regression (LWSVR), kernel density estimated trend (KDET), and a convolutional neural network (CNN) are incorporated as secondary variables within intrinsic collocated cokriging (ICCK). Additionally, the study utilizes two types of ensemble models—global (GWE) and local weighting-based (LWE) ensembles. These ensembles integrate outputs from hybrid models, applying global and local weights based on each model’s cross-validation performance. Depending on their level of expertise, humans are integrated as either (1) novice practitioners considered as human-as-feedback (HAF) systems where they act as model checks and key parameter validators, without the ability to influence ML training or (2) expert practitioners considered as systems where model parameters are actively adjusted, model structures are tuned, and the learning process is guided by human experts. The effectiveness of the HAF and HAC systems is evaluated using data from multiple blast areas obtained from an open-pit copper mine. Compared to fully automated modelling, the HAF system improved estimation accuracy in terms of R 2 values by between 3.6% ( ICCK CNN ) and 5.9% (GWE) across hybrid and ensemble models. Meanwhile, the HAC system demonstrated more significant enhancements, with R 2 values increase ranging from 5.0% ( ICCK CNN ) to 16.5% (GWE) for these same models. This advancement suggests the potential for more precise and effective decision-making in mining operations using HITL systems. • Human-in-the-Loop (HITL) Integration: Enhances human-AI collaboration in mineral grade estimation. • Advanced ML Models: Utilizes ML-based geostatistical hybrid models and ensemble approaches for improved spatial estimation accuracy. • User Interaction Systems: Introduces human-as-feedback (HAF) for novices and human-as-controller (HAC) for experts, enabling tailored interactions. • Improved Prediction Accuracy: Demonstrates significant improvements in mineral grade predictions using data from an open-pit copper mine. • Decision-Making in Mining: Shows the potential for more precise and effective decision-making through tailored human-AI systems in mining operations.