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Geospatial Data Modeling For Mineral Exploration In Saghand-Chadormalu Area, Central Iran

Posted on:2005-06-10Degree:DoctorType:Dissertation
Country:ChinaCandidate:Pouran BehniaFull Text:PDF
GTID:1100360125455731Subject:Photogrammetry and Remote Sensing
Abstract/Summary:PDF Full Text Request
The objective of this study is to establish a practical mineral exploration model for Proterozoic mineralization of Fe, P, U, Th, and REEs in Saghand-Chadormalu area, Central Iran, and apply GIS based quantitative methods for characterizing geo-explosion data sets, and for integrating different data sets to generate maps showing favorable mineralized zones.Central Iran is considered as a part of Gondwanaland and is one of the oldest blocks in Iran. The strata exposed in this region include late Proterozoic, Paleozoic, Mesozoic and Cenozoic rock units. The tectonic history of Saghand-Bafq block can be divided into three periods: pre-rifting, rifting and post-rifting periods. During Late Proterozoic period, the Central Iran block was in a relatively stable status and Natk terrigenous formation was formed. By the end of Late Proterozoic, the relatively stable Central Iran block was affected by mantle doming and development of nearly N-S trending riftogenic structures. Saghand and Rizu series are supposed to be deposited in deep grabens of riftogenic structures. Saghand-Bafq block was influenced by high-pressure dynamo-metamorphism associated with deformation in Mesozoic as well as high-grade dynamo-thermal activity in Eocene occurring as a result of Neothetys oceanic crust subduction beneath the central continental crust. Mineralization of Fe, P, U, Th, and REEs has been attributed to Tectono-Magmatic Activization occurring in Late Proterozoic.Comparison of iron oxide P-U-Th-REE bearing deposits in Central Iran with Proterozoic iron oxide (CU,-U-AU-REE) deposits proposed by Hitzman suggest that mineralization in Central Iran has most common genetic features of this type of mineralization. Based on detailed characteristics of mineralization in known deposits of the area, Proterozoic mineralization in Central Iran can be further divided into four types: Magmatogenetic mineralization of magnetite and magnetite apatite; metasomatic Fe, U, Th, REE, Ti and apatite ore mineralization; hydrothermal subvolcanic related U, Mo, Au, Cu, Co, Pb, Zn, and As mineralization; and exhalative Pb-Zn mineralization.Based on the mineral deposit model the critical recognition criteria (CRC) for predictive mapping of favorable zones were suggested. The main CRC for magmatogenetic mineralization are deduced to be 1) presence of high magnetic field, high total count and very high ratio of Th/U and 2) proximity to intersections of deep N-S and E-W striking crustal structures. The CRC for predictive mapping of favorable zones of metasomatic Fe, U, Th, REE, Ti and apatite ore mineralization are summarized as 1) presence of favorable host lithology i.e. Saghand series; 2) presence of K-Na-Si-metasomatite zones; 3) proximity to Zarigan-type granites; intersections of deep N-S and E-W striking crustal structures; and 4) presence of high magnetic fields, spatial association with high U, Th, and low K values. The CRC for predictive mapping of favorable zones of hydrothermal subvolcanic related U, Mo, Au, Cu, Co, Pb, Zn, and As mineralization are deduced as 1) presence of favorable host rocks i.e. Saghand series; 2) presence of low-sulfide alteration; 3) spatial association with high total count, U, and K values; and 4) presence of anomalously high values for Cu, Au, Mo, As, Co, Pb, and Zn in associated rocks and in stream sediments.The geo-exploration data sets available for the study area comprise the geologic maps, airborne geophysical data, remote sensing data, geochemical analysis data and topographic maps. The operations employed for extraction of CRC involve map generalization; creating proximity maps to curvilinear and point geological objects; creating DEM from topographic data; creating catchments basin map; applying concentration-area method to determine thresholds of geochemical distributions for anomaly separation on the basis of the spatial relationship of abundance; applying filtering operations on geophysical data and creation of derivative maps; image mosaicking and rectification; comparison of different methods on data fus...
Keywords/Search Tags:GIS, RS, Mineral potential mapping, Weights of evidence, Logistic regression, Fuzzy logic, Neural network
PDF Full Text Request
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