Ore grades are monitored regularly in cave mines through drawpoint sampling. Automating grade monitoring through deploying X-ray fluorescence (XRF) sensors on the buckets of production loaders has been proposed as an alternative approach to address the issues around the traditional practice of drawpoint sampling. Bucket-mounted sensors can also be employed for bulk ore sorting underground. This study is aimed at evaluating the deployment of XRF sensors on production loaders as an opportunity for grade monitoring or bulk ore sorting in caving operations. The mill feed grade prediction performances of the drawpoint sampling program and mine planning software were assessed for the Cadia East panel cave mine. The results showed that the drawpoint samples underestimated the mill feed quality during a 10-month investigation period. The cave portions with bulk ore sorting potential were linked to the extraction level layout to estimate the number of drawpoints where sensors could be situated for diverting ore and waste. Samples obtained from the mine were tested to evaluate the ability of a lab-scale proxy of a bucket-mounted XRF sensor system to measure copper and gold grades. R-squared values of 0.84 and 0.68 were achieved between the predicted and measured copper and gold grades of the samples, respectively. Sensor test results are promising in revealing the potential to utilize XRF sensors underground. Future test work is encouraged to further validate the applicability of XRF sensors in an underground mining environment.

Conceptual bulk ore sorting studies are essential for determining a potential improvement in mine economics before undertaking on-site sensor trials. Two approaches, block modelling and drill core compositing, are applied to simulate the bulk ore sorting performance of mining operations. While one employs the grade data of a block model, the other approach utilizes composited drill core grades. This study aimed at comparing these two approaches by estimating in-situ grade heterogeneities and simulating the bulk ore sorting performances of the currently active caves of the Cadia East panel cave mine. The results show that block modelling tends to smooth the grade variability that initially exists in the drill core grade data. Particularly in the portions of the deposit where drilling is sparse or widely spaced compared to the selected block size, block modelling leads to lower grade heterogeneity and bulk ore sorting performance estimates. However, when the drill hole data is nonrepresentative of the area of interest, block modelling can predict more realistic bulk ore sorting performances compared to drill core grades. The assessments performed with the blocks and drill core composites of various sizes showed that grade heterogeneity was adversely affected by an increased sorting scale due to averaged metal grades.