Rapid estimation of porosity and mineral abundance in backscattered electron images using a simple SEM image analyser

Pye, Kenneth

doi:10.1017/s0016756800028041

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…Fe, Mg, Ca and Mn are expressed as molar carbonate fractions. Siderite abundance was determined with BSEM digital images of the polished surfaces (at magnification ×300), as described by Pye (1984). The images were separated into siderite, pyrite and calcite + clay + quartz + porosity windows using the LINK software.…”

Section: Methodsmentioning

confidence: 99%

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

Hounslow¹

2001

Sedimentology

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The crystallographic fabric of siderite in siderite concretions has been determined for upper Carboniferous (Westphalian‐A) non‐marine concretions and lower Jurassic (Pliensbachian) marine concretions. Compositional zoning indicates that individual siderite crystals grew over a period of changing pore water chemistry, consistent with the concretions being initially a diffuse patch of cement, which grew progressively. The siderite crystallographic fabric was analysed using the anisotropy of magnetic susceptibility, which is carried by paramagnetic siderite. The siderite concretions from marine and non‐marine formations exhibit differences in fabric style, although both display increases in the degree of preferred siderite c‐axis orientation towards the concretion margins. The Westphalian non‐marine siderites show a preferred orientation of siderite c‐axes in the bedding plane, whereas the Pliensbachian marine siderites have a preferred orientation of c‐axes perpendicular to the bedding. In addition, a single marine concretion shows evidence of earlier formed, inclined girdle‐type fabrics, which are intergrown with later formed vertical c‐axis siderite fabrics. The marine and non‐marine fabrics are both apparently controlled by substrate processes at the site of nucleation, which was probably clay mineral surfaces. Siderite nucleation processes on the substrate were most probably controlled by the (bio?) chemistry of the pore waters, which altered the morphology and crystallographic orientation of the forming carbonate. The preferred crystallographic orientation of siderite results from the orientation of the nucleation substrate. Fabric changes across the concretions partially mimic the progressive compaction‐induced alignment of the clay substrates, while the concretion grew during burial.

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Section: Methodsmentioning

confidence: 99%

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

Hounslow¹

2001

Sedimentology

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“…Petrographic and microbeam approaches focus on individual grains, incorporating varied geochemical and morphological parameters for mineral determinations (Minnis 1984). Scanning electron microscopy (SEM) using backscattered electron (BSE) and energy-dispersive X-ray (EDS) spectrometry images have been used to map elements and to determine grain separations, boundaries, and shapes (Hall and Lloyd 1981;White and others 1984;Pye 1984;Pye and Krinsley 1984;Dilks and Graham 1985;Mainwaring 1989;Tovey and others 1989;Petruk 1989aPetruk , 1989bNadeau and Hurst 1991;Krinsley 1991, 1992;Protz and others 1992;Tovey and Hounslow 1995;Krinsley and others 1998). EDS images map the distribution of elements among mineral grains, whereas BSE images relate grain brightness to mean atomic number.…”

Section: Introductionmentioning

confidence: 99%

Mineralogy of fine-grained sediment by energy-dispersive spectrometry (EDS) image analysis a methodology

Knight

Klassen

Hunt

2002

Environmental Geology

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The application of energy-dispersive X-ray (EDS) spectrometry image analyses to geological, geochemical, and environmental studies is facilitated by recent increases in computer speed, storage capacity, and connectivity. EDS microbeam analyses provide digital element maps of mineral grains, with brightness directly proportional to element abundance. Using computer-based image analysis protocols, combinations of EDS element images can characterize grain mineralogy and quantify mineral abundance. This approach is well suited to the analyses of fine sand and silt-sized grains (0.425-0.010 mm), a size fraction not amenable to quantification by other mineralogical analysis methods. For this size range, 50 to more than 300 grains can be readily mapped, permitting mineral identification for concentrations >1% to be reliably distinguished.

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“…The BSE image can be acquired with a scanning electron microscope (SEM) in a short time. The image can be used to distinguish minerals on the basis of differences in BSE intensity, which is strongly dependent on the average atomic number of the target (Robinson & Nickel 1979, Hall & Lloyd 1981, Pye 1984, Dilks & Graham 1985, Petruk 1988, 1989, Lastra et al 1998). …”

Section: Introductionmentioning

confidence: 99%

An Algorithm for the Transformation of XRF Images Into Mineral-Distribution Maps

Togami¹,

Takano²,

Kumazawa³

et al. 2000

The Canadian Mineralogist

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A new numerical method has been developed that uses the pixel-intensities from X-ray fluorescence (XRF) images to produce mineral-distribution images or maps. The XRF images are obtained using a scanning X-ray analytical microscope (SXAM); the derived mineral-distribution maps can be applied to study the petrographic characterization of a rock or ores. As a test case, we applied this method to a granite consisting dominantly of quartz, biotite, plagioclase and K-feldspar. Maps of the major elements, including Al, Si, K, Ca and Fe, were obtained with the SXAM. XRF intensity is recorded for each element on a 256 ϫ 256 pixel map. To transform these element-distribution maps into mineral-distribution maps, we employed the maximum likelihood method for a Gaussian distribution, i.e., a least-squares method. The coefficient between XRF intensity and the proportion of a desired mineral was determined from the average value in a few hundred pixels that record the XRF intensity of the desired mineral in isolation. Where one pixel recorded XRF intensity for more than one mineral, its intensity was assumed to have a linear relationship with the composition of the minerals included in the pixel. The proposed least-squares method is an alternative technique to methods using purely image-analysis operations, such as image enhancements, erosions, dilations and image Boolean operations. Experiments with the granite sample showed that the least-squares method gives appropriate mineral-distribution maps if the acquisition time for the XRF maps is sufficiently long, e.g., 48 hours for the granite. The sources of errors in the calculated proportions of the minerals are related to fluctuations of X-ray intensities and variations of chemical composition in each mineral. A method is also proposed to estimate these errors.Keywords: granite, least-squares method, mineral-distribution map, scanning X-ray analytical microscope, XRF image. SOMMAIRENous avons développé une nouvelle méthode numérique fondée sur l'intensité des pixels contenant les images de fluorescence X pour produire des images ou des cartes de la distribution de minéraux. Les images de la fluorescence X sont obtenues avec un microscope analytique à balayage de rayons X. Les cartes dérivées montrant la distribution de minéraux peuvent servir à la caractérisation pétrographique d'une roche ou d'un minerai. Nous avons appliqué cette méthode à l'étude d'un granite contenant surtout quartz, biotite, plagioclase et feldspath potassique. Des cartes de la distribution des éléments majeurs, dont Al, Si, K, Ca et Fe, ont été préparées selon l'intensité de la fluorescence X de chaque élément enregistrée avec le microscope analytique et projetée sur une carte de 256 ϫ 256 pixels. Afin de transformer ces cartes de distribution d'éléments en cartes illustrant la distribution de minéraux, nous employons la méthode de la probabilité maximum pour une distribution gaussienne, c'est-à-dire, une méthode par moindres carrés. Le coefficient de concordance entre l'intensité de la fluores...

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Rapid estimation of porosity and mineral abundance in backscattered electron images using a simple SEM image analyser

Cited by 13 publications

References 9 publications

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

Mineralogy of fine-grained sediment by energy-dispersive spectrometry (EDS) image analysis a methodology

An Algorithm for the Transformation of XRF Images Into Mineral-Distribution Maps

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Rapid estimation of porosity and mineral abundance in backscattered electron images using a simple SEM image analyser

Cited by 13 publications

References 9 publications

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

The crystallographic fabric and texture of siderite in concretions: implications for siderite nucleation and growth processes

Mineralogy of fine-grained sediment by energy-dispersive spectrometry (EDS) image analysis  a methodology

An Algorithm for the Transformation of XRF Images Into Mineral-Distribution Maps

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Product

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Mineralogy of fine-grained sediment by energy-dispersive spectrometry (EDS) image analysis a methodology