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Petroleum Geochemical Survey
Published in Muhammad Abdul Quddus, Petroleum Science and Technology, 2021
An oxide mineral consists of a closely packed structure of oxygen atoms in which positively charged cations are located in the interstices (narrow space). Silicon dioxide (SiO2) is the most important and abundant oxide existing in nature. The purest form of silicon dioxide is quartz. The next most important oxide is aluminum oxide (Al2O3), known as alumina. Both silicon and aluminum oxides are found in earth in different polymorphic forms. Sand is the crushed form of quartz. Sandstone consists of sand particles bonded together. Kieselguhr rock is made of silicon dioxide which is the remains of siliceous marine organisms. The bond between silicon and oxygen is covalent. Silicon atoms are bonded tetrahedrally to four oxygen atoms. Silicon dioxide is very hard, having a high melting point. It is the hardest substance having a crystalline structure used as a semiconductor. Aluminum oxides occur naturally in several crystalline polymorphic forms. The precious gemstones ruby and sapphire are polymorphic forms of alumina. Hematite and magnetite are well-known iron oxide minerals. Magnesium oxide (magnesia) is a white hygroscopic mineral. Pyrolusite` and ramsdellite are polymorphic minerals of manganese dioxide. In addition to these, several hundred natural oxides of various types are known. Oxides are formed either by covalent linkage or by ionic bond between oxygen free radicals (O)–2 and one or more metallic free radicals (cations). The carborundum mineral is silicon carbide (SiC) and occurs in nature as traces.
Remote Sensing of Soil in the Optical Domains
Published in Prasad S. Thenkabail, Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, 2015
Iron associated with clay mineral structures is also an active chromophore in both the VIS-NIR and SWIR spectral regions. is can be seen in the nontronite-type mineral presented in Figure 25.24. Based on the structural OH-Fe features of smectite in the SWIR region, Ben-Dor and Banin (1990a) generated a predictive equation to account for the total iron content in a series of smectite minerals. e wavelengths that were selected automatically by their method were 2.2949, 2.2598, 2.2914, and 1.2661 μm. Stoner (1979) also observed a higher correlation between re¨ectance in the 1.55-2.32 μm region and iron content in soils, whereas Coyne et al. (1989) found a linear relationship between total iron content in montmorillonite and absorbance measured in the 0.6-1.1 μm spectral region. BenDor and Banin (1995a) used spectra of 91 arid soils to show that their total iron content (both free and structural iron) can be predicted by multiple linear regression analysis and wavelengths 1.075, 1.025, and 0.425 μm. Obukhov and Orlov (1964) generated a linear relationship between re¨ectance values at 0.64 μm and the total percentage of Fe2O3 in other soils. Taranik and Kruse (1989) showed that a binary encoding technique for the spectral-slope values across the VIS-NIR spectral region is capable of di¦erentiating a hematite mineral from a mixture of hematite-goethite-jarosite. It is important to mention that iron can o¬en have an indirect in¨uence on the overall spectral characteristics of soils. In the case of free Fe oxides, it is well known that soil particle size is strongly related to absolute Fe oxide content (Soileau and McCraken, 1967; Stoner and Baumgardner, 1981; Ben-Dor and Singer, 1987): as the Fe oxide content increases, the size fraction of the soil particles increases as well, because of the cementing e¦ect of the free Fe oxides. As a result, problems resulting from di¦erent scattering e¦ects are introduced into the soil analysis. Moreover, free Fe oxides, mostly in their amorphous state, can coat the soil particles with a ¤lm that prevents natural interaction between the soil particle (clay or nonclay minerals) and the sun’s photons. Fe oxide minerals can be indicators for soil-stabilization processes (Ben-Dor et al., 2005). Karmanova (1981) found that well-crystallized iron compounds have the strongest e¦ect on the spectral re¨ectance of soil and that removal of nonsilicate iron (mostly Fe oxides) helps enhance other chromophores in the soil. In this respect, Kosmas et al. (1984) demonstrated a second-derivative technique in the VIS region as a feasible approach for di¦erentiating even small features of synthetic goethite from clays, and they suggested
Copper recovery improvement in an industrial flotation circuit: A case study of Sarcheshmeh copper mine
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019
Mehrshad Asghari, Fardis Nakhaei, Omid VandGhorbany
It should be noted that the feed size distributions of rougher flotation cells are almost similar (65% < 74 µm) in both surveys. Mineralogy analysis showed that chalcopyrite is the major phase of the copper sulfide mineral (1.7%) and chalcocite (0.3%) and covellite (0.08%) are the minor phases. Oxide minerals were cuprite, tenorite, chrysocolla, malachite, and azurite. Also, other minerals such as pyrite (6.6%), limonite (0.02%), and molibdenite (0.05%) were distinguished in the representative sample. Total mineral non-metals such as quartz, illite, chlorite, orthoclase, albeit, and muscovite were equal to 92%.
Identification and mapping of high-potential iron ore alteration zone across Joda, Odisha using ASTER and EO-1 hyperion data
Published in Journal of Spatial Science, 2019
Arnab Sengupta, Manik Das Adhikari, Sabyasachi Maiti, Soumya Kanti Maiti, Pankajini Mahanta, Siddhartha Bhaumick
The various lithological mapping such as identification of granites (Watts and Harris 2005, Massironi et al. 2008, Son et al. 2014), ophiolite sequences (Van Deer Meer and De Jong 2012) and basement rocks (Gad and Kusky 2007, Qari et al. 2008) on ASTER data are widely available on several studies. Band ratios can be utilized to apprehend the physiochemistry of the investigated material through which the depth and shape of the mineral-related absorption features in VNIR, SWIR and TIR can be obtained. BR is a technique where the DN value of one band is divided by the DN value of another band and is very convenient for emphasizing certain features which cannot be visible in the raw bands (Inzana et al. 2003). In the recent years, it is observed that several researchers for geological mapping have widely accepted ASTER BR technique (Gad and Kusky 2007, Khan et al.2007, Aboelkhair et al. 2010, Amer et al. 2010, Pour and Hashim 2011). The AlOH group of minerals, which comprises albearing hydroxylated sheet silicates, such as muscovite, illite, phengite, kaolinite, and Al smectite, were identified based on a band ratio of (b5 + b7)/b6. In contrast, the AlOH group composition was measured by using a ratio of b5/b7, and was masked to include those pixels where there is an adequate amount of AlOH group of minerals. A comprehensive gestalt of different mineral indices on ASTER multispectral data is described in ASTER mineral index processing manual as compiled by Kalinowski and Oliver (2004) and it also recommends a range of band combinations and false colour composites for the identification of various mineral groups that highlight alteration intensity (Van Deer Meer and De Jong 2012). In this study, emphasis has been given on ASTER data which highlights the presence of iron ore zones in BIFs that contains (i) Ferric iron oxide minerals (e.g. hematite and goethite) and (ii) Ferrous iron in silicate and/or carbonate minerals (e.g. actinolite, chlorite, and siderite).Therefore, as proposed by Van Deer Meer and De Jong (2012), we have used Ferric Iron, Ferrous Iron, Ferrous Silicates and Ferric Oxide Index i.e. band 2/band 1, band 5/band 3, band 5/band 4 and band 4/band 3 respectively for the semi-automatic mapping of iron ore deposit in the study area as depicted in Figure 7. Figure7(a-d) also depicts that the high-intensity colour epitomizes the concentration of ferric iron, ferrous iron, ferrous silicates and ferric oxide deposited areas.