Business Challenges n the current [mining] economic climate, minimizing costs...
Predictive Modelling of Nickel Potential with the Integration of Multisource Information Based on Random Forest
Predictive Modelling of Nickel Potential with the Integration of Multisource Information Based on Random Forest
- September 7, 2022
- Posted by: admina
- Category: Uncategorized
Business Challenges
Mineral exploration activities require robust predictive models that accurately map the possibility that mineral deposits can be found at as pecific location.
Suggested Solution
Random forest (RF) is a powerful machine data-driven predictive technique unknown in potential mineral mapping. This project explores the performance of RF regression for the nickel deposits in the vale mine sites in Sudbury, Ontario, Canada. The results of this project indicate that the use of RF for the integration of sizeable multisource data sets used in mineral exploration and for the prediction of mineral deposit occurrences offers several advantages over existing methods. RF’s key benefits include the simplicity of parameter setting, an internal unbiased estimate of the prediction error, the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; the capability to use categorical predictors; and the capacity to determine variable importance. Variables that RF identified as most significant coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, nickel prospectively mapsare also prepared using the Logistic Regression (LR) method.
Project Info
Start Date
July 2022
DurationÂ
36 (Months)
Location
Brazil–Spain
Keywords
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