Pages that link to "Item:Q732185"
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The following pages link to Simulation of mineral grades with hard and soft conditioning data: application to a porphyry copper deposit (Q732185):
Displaying 17 items.
- Co-simulating total and soluble copper grades in an oxide ore deposit (Q452220) (← links)
- Analysis and estimation of multi-unit deposits: application to a porphyry copper deposit (Q500778) (← links)
- A top-cut model for deposits with heavy-tailed grade distribution (Q500784) (← links)
- Forecasting recoverable ore reserves and their uncertainty at Morila gold deposit, Mali: an efficient simulation approach and future grade control drilling (Q500787) (← links)
- Construction and efficient implementation of adaptive objective-based designs of experiments (Q748105) (← links)
- Grade control classification of ore and waste: a critical review of estimation and simulation based procedures (Q812108) (← links)
- Probabilistic modeling of ore lens geometry: an alternative to deterministic wireframes (Q812115) (← links)
- Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations (Q883152) (← links)
- Localised uniform conditioning (LUC): a new approach for direct modelling of small blocks (Q883157) (← links)
- New indices for characterizing spatial models of Ore deposits by the use of a sensitivity vector and an influence factor (Q883166) (← links)
- Comparison of machine learning methods for copper ore grade estimation (Q1715372) (← links)
- Geostatistical simulation of geochemical compositions in the presence of multiple geological units: application to mineral resource evaluation (Q1740326) (← links)
- Conditional simulation for mineral resource classification and mining dilution assessment from the early 1990s to now (Q2022113) (← links)
- Integration of uncertain data in geostatistical modelling (Q2360995) (← links)
- A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration (Q2398596) (← links)
- Transformation of residuals to avoid artifacts in geostatistical modelling with a trend (Q2457846) (← links)
- Modeling geospatial uncertainty of geometallurgical variables with Bayesian models and Hilbert-kriging (Q6084314) (← links)
