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Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain

Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain
Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain
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
1365-8816
1336-1354
Rodriguez-Galiano, V.F.
1eb6a1dd-f73d-4e90-a9cf-a51f20712c3c
Chica-Olmo, M.
c7291c15-3b53-45d7-942c-06985f77d6f6
Chica-Rivas, M.
83b33f96-13c6-4448-b8b5-ad4fe1e81115
Rodriguez-Galiano, V.F.
1eb6a1dd-f73d-4e90-a9cf-a51f20712c3c
Chica-Olmo, M.
c7291c15-3b53-45d7-942c-06985f77d6f6
Chica-Rivas, M.
83b33f96-13c6-4448-b8b5-ad4fe1e81115

Rodriguez-Galiano, V.F., Chica-Olmo, M. and Chica-Rivas, M. (2014) Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain. International Journal of Geographical Information Science, 28 (7), 1336-1354. (doi:10.1080/13658816.2014.885527).

Record type: Article

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

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Published date: 1 April 2014
Organisations: Global Env Change & Earth Observation

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Local EPrints ID: 370076
URI: http://eprints.soton.ac.uk/id/eprint/370076
ISSN: 1365-8816
PURE UUID: f099e6af-1717-4a92-9cdc-19d29ce2703d

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Date deposited: 23 Oct 2014 10:46
Last modified: 14 Mar 2024 18:12

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Contributors

Author: V.F. Rodriguez-Galiano
Author: M. Chica-Olmo
Author: M. Chica-Rivas

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