Paper Info
Title
Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain
Abstract
Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered.
Year
DOI
Venue
2014
10.1080/13658816.2014.885527
International Journal of Geographical Information Science
Keywords
Field
DocType
random forest,mineral potential mapping,mineral prospectivity,mineral exploration,data-driven models
Thematic Mapper,Data mining,Data set,Magnetic survey,Regression,Prospectivity mapping,Computer science,Remote sensing,Mineral exploration,Predictive modelling,Random forest
Journal
Volume
Issue
ISSN
28
7
1365-8816
Citations 
PageRank 
References 
10
0.63
12
Authors
3
Name
Order
Citations
PageRank
V. F. Rodriguez-Galiano1394.09
Mario Chica-Olmo2515.96
Mario Chica-Rivas3292.22