2023 - Research.com Earth Science in South Africa Leader Award
2022 - Research.com Earth Science in South Africa Leader Award
Prospectivity mapping, Geochemistry, Mineralogy, Data mining and Spatial analysis are his primary areas of study. Emmanuel John M. Carranza combines subjects such as Data-driven and Random forest with his study of Prospectivity mapping. His Geochemistry research incorporates themes from Mineralization, Stockwork and Fault.
Emmanuel John M. Carranza has researched Mineralogy in several fields, including Sediment, Hydrothermal circulation, Mineral exploration, Frequency distribution and Anomaly. Emmanuel John M. Carranza has included themes like Fuzzy set, Membership function, Fuzzy logic, Artificial intelligence and Plot in his Data mining study. His Spatial analysis study combines topics from a wide range of disciplines, such as Prediction rate and Geographic information system.
The scientist’s investigation covers issues in Geochemistry, Prospectivity mapping, Mineralogy, Mineralization and Mineral exploration. His Geochemistry research focuses on Galena and how it relates to δ34S and Evaporite. His Prospectivity mapping study also includes fields such as
His studies in Mineralogy integrate themes in fields like Sediment, Anomaly and Principal component analysis. Mineralization is a subfield of Hydrothermal circulation that he explores. His Mineral exploration study frequently draws connections to adjacent fields such as Mineral deposit.
His primary scientific interests are in Geochemistry, Prospectivity mapping, Mineral resource classification, Mineral exploration and Mineral. His Geochemistry research is multidisciplinary, incorporating elements of Tetradymite, Fluid inclusions and Petzite. His Prospectivity mapping study incorporates themes from Mineral deposit, Data mining, Support vector machine, Fuzzy logic and Random forest.
His Mineral resource classification research includes elements of Ellipse, Window and Sustainable development. His study in Mineral exploration is interdisciplinary in nature, drawing from both Multi-source, Earth science, Kriging and Interpolation. His Metamorphic rock course of study focuses on Mineralization and Mineralogy.
His primary areas of study are Geochemistry, Mineral resource classification, Prospectivity mapping, Mineral exploration and Algorithm. His research brings together the fields of Multi-source and Geochemistry. His research integrates issues of Carbonate rock, Window and Multifractal system in his study of Mineral resource classification.
His studies deal with areas such as Sedimentary rock, Mineral deposit, Metamorphic rock and Precambrian as well as Prospectivity mapping. He interconnects Wall rock, Hydrothermal circulation, Computer simulation and Petrology in the investigation of issues within Mineral exploration. The concepts of his Algorithm study are interwoven with issues in Exploratory data analysis, Multivariate statistics and Fractal analysis.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Arsenic geochemistry and health
Alfred A Duker;E J M Carranza;Martin Hale.
Environment International (2005)
Geochemical Anomaly and Mineral Prospectivity Mapping in Gis
Emmanuel John M. Carranza.
(2012)
Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)
Jamshid Farifteh;F Van der Meer;C Atzberger;E. J. M Carranza.
Remote Sensing of Environment (2007)
Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values
Emmanuel John M. Carranza.
Journal of Geochemical Exploration (2011)
Support vector machine: A tool for mapping mineral prospectivity
Renguang Zuo;Emmanuel John M. Carranza.
Computers & Geosciences (2011)
Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India
Alok Porwal;E. J. M. Carranza;M. Hale;M. Hale.
Natural resources research (2003)
Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features
Emmanuel John M. Carranza.
Ore Geology Reviews (2009)
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping
Alok Porwal;E. J. M. Carranza;M. Hale;M. Hale.
Natural resources research (2003)
Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines
Emmanuel John M. Carranza;Martin Hale.
Ore Geology Reviews (2003)
Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling
Mahyar Yousefi;Emmanuel John M. Carranza.
Computers & Geosciences (2015)
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