Geochemical Anomaly and Mineral Prospectivity Mapping in Gis

By Emmanuel John Muico Carranza. 2012

652 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/491c245dace7d6ca3db8e61aa8636a4effde53c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Assessment of Various Fuzzy c-Mean Clustering Validation Indices for Mapping Mineral Prospectivity: Combination of Multifractal Geochemical Model and Mineralization Processes (2019, methodology): “A plot have been adopted for evaluation of prospectivity models in different MPM studies (e.g., Agterberg and Bonham-Carter 2005; Carranza 2008; Yousefi and Carranza 2015).”
Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, methodology): “The analysis of the spatial relationships between evidential features and known deposit locations is carried out bymeans of different numerical methods (Bonham-Carter, 1994; Carranza, 2008).”
Support vector machine: A tool for mapping mineral prospectivity (2011, methodology): “Geocomputational techniques for mineral prospectivity mapping can be categorized generally into two types – knowledgedriven and data-driven – according to the type of inference mechanism considered (Bonham-Carter 1994; Pan and Harris 2000; Carranza 2008).”
Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, methodology): “…(i) weights-of-evidence (WofE) modeling (Bonham-Carter et al., 1988, 1989; Agterberg et al., 1990, 1993); and (ii) evidential belief modeling (An et al., 1994a,b; Carranza and Hale, 2003; Carranza et al., 2005, 2008a, 2008b, 2009; Carranza and Sadeghi, 2010; Carranza, 2009a, 2011a, 2014).”