D-Index & Metrics Best Publications
Earth Science
South Africa
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Earth Science D-index 69 Citations 14,251 288 World Ranking 635 National Ranking 1

Research.com Recognitions

Awards & Achievements

2023 - Research.com Earth Science in South Africa Leader Award

2022 - Research.com Earth Science in South Africa Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

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.

His most cited work include:

  • Arsenic geochemistry and health (414 citations)
  • Geochemical Anomaly and Mineral Prospectivity Mapping in Gis (311 citations)
  • Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN) (249 citations)

What are the main themes of his work throughout his whole career to date?

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

  • Data mining, which have a strong connection to Fuzzy logic,
  • Spatial analysis together with Geographic information system.

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.

He most often published in these fields:

  • Geochemistry (71.29%)
  • Prospectivity mapping (25.81%)
  • Mineralogy (27.10%)

What were the highlights of his more recent work (between 2019-2021)?

  • Geochemistry (71.29%)
  • Prospectivity mapping (25.81%)
  • Mineral resource classification (13.23%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm (15 citations)
  • 3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China (11 citations)
  • Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data (8 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

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.

Best Publications

Arsenic geochemistry and health

Alfred A Duker;E J M Carranza;Martin Hale.
Environment International (2005)

697 Citations

Geochemical Anomaly and Mineral Prospectivity Mapping in Gis

Emmanuel John M. Carranza.
(2012)

682 Citations

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)

469 Citations

Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values

Emmanuel John M. Carranza.
Journal of Geochemical Exploration (2011)

281 Citations

Support vector machine: A tool for mapping mineral prospectivity

Renguang Zuo;Emmanuel John M. Carranza.
Computers & Geosciences (2011)

279 Citations

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)

266 Citations

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)

261 Citations

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)

231 Citations

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)

215 Citations

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)

214 Citations

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