Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
chemistry
Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping
Geochemistry: Exploration, Environment, Analysis, Volume 14, No. 1, Year 2014
Notification
URL copied to clipboard!
Description
Stream sediment geochemical data are usually subjected to methods of multivariate analysis (e.g. factor analysis) in order to extract an anomalous geochemical signature (factor) of the mineral deposit-type sought. A map of anomalous geochemical signature can be used as evidence, in combination with other layers of evidence, for mineral prospectivity mapping (MPM). Because factor analysis may yield more than one factor in a stream sediment dataset, it raises the challenge of how to recognize the factor that best indicates presence of the mineral deposit-type sought. In addition, MPM is faced with the challenge of how to assign weights to classes in a geochemical evidence map. Accordingly, a new approach is discussed in this paper for the extraction of significant anomalous geochemical signature of the mineral deposit-type sought and for assigning weights to anomaly classes in a geochemical evidence map. In this approach, we used a staged factor analysis and then applied a logistic function to transform factor scores representing an anomalous geochemical signature in order to derive a map of geochemical mineralisation prospectivity indices (GMPI) as a spatial evidence layer for MPM based on the theory of fuzzy sets and fuzzy logic. The GMPI is a fuzzy weight in the [0,1] range. We demonstrate the application of the GMPI for mapping prospectivity for Mississippi valley-type fluorite deposits in the Mazandaran province, north of Iran, which is a greenfield area. © 2014 AAG/The Geological Society of London.
Authors & Co-Authors
Yousefi, Mahyar
Iran, Malayer
Malayer University
Carranza, Emmanuel John M.
Netherlands, Enschede
Universiteit Twente
Australia, Townsville
James Cook University
Statistics
Citations: 133
Authors: 2
Affiliations: 4
Identifiers
Doi:
10.1144/geochem2012-144
ISSN:
14677873