Semi-automated procedure of digitalization and study of rock thin section porosity applying optical image analysis tools

Berrezueta, Edgar; Domínguez-Cuesta, María José; Rodríguez-Rey, A.

doi:10.1016/j.cageo.2018.12.009

Cited by 21 publications

(8 citation statements)

References 42 publications

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“…In this last case, two microdomains (i.e., C1, C2) have been selected as the best candidates to execute the tools. These tools are based on various scripts and functions adopted for images analysis, implemented within the most common GIS software, which have been increasingly used in solving various geoscientific issues (e.g., Li et al, 2010;DeVasto et al, 2012;Pradhan, 2013;Ortolano et al, 2014a,b;Belfiore et al, 2016;Fiannacca et al, 2017;Berrezueta et al, 2019).…”

Section: Chemical Imaging and Fabric Analysismentioning

confidence: 99%

Integrating X-Ray Computed Tomography With Chemical Imaging to Quantify Mineral Re-crystallization From Granulite to Eclogite Metamorphism in the Western Italian Alps (Sesia-Lanzo Zone)

et al. 2019

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Metamorphic transformations and fabric evolution are the consequence of thermodynamic processes, lasting from thousands to millions of years. Relative mineral percentages, their grain size distribution, grain orientation, and grain boundary geometries are first-order parameters for dynamic modeling of metamorphic processes. To quantify these parameters, we propose a multidisciplinary approach integrating X-ray computed microtomography (µ-CT) with X-ray chemical mapping obtained from an Electron MicroProbe Analyzer (EMPA). We used a metapelitic granulite sample collected from the Alpine HP-LT metamorphic rocks of the Mt. Mucrone (Eclogitic Micaschists Complex, Sesia-Lanzo Zone, Western Alps, Italy). The heterogeneous Alpine deformation and metamorphism allowed the preservation of pre-alpine structural and mineralogical features developed under granulite-facies conditions. The inferred granulitic mineral association is Grt + Bt + Sil + Pl + Qtz ± Ilm ± Kfs ± Wm. The subsequent pervasive static eclogite-facies re-equilibration occurred during the alpine evolution. The inferred alpine mineral association is Wm + Omp ± Ky + Qtz + Grt though local differences may occur, strongly controlled by chemistry of microdomains. X-ray µ-CT data extracted from centimeter-sized samples have been analyzed to quantify the volumetric percentage and shape preferred orientation (SPO) for each mineral phase. By combining tomographic phase separation with chemical variation and microstructures (i.e., different grain-size classes for the same phase and morphology of different pre-alpine microdomains) the pre-alpine mineralogical phases from the alpine overprint have been distinguished and quantified. Moreover, the sample preserves 100% of the pre-alpine granulite fabric, which surprisingly corresponds to less than 22% of the corresponding pre-alpine metamorphic assemblages, while the alpine eclogitic static assemblage corresponds to 78% though no new fabric is developed. This contribution

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Section: Chemical Imaging and Fabric Analysismentioning

confidence: 99%

Integrating X-Ray Computed Tomography With Chemical Imaging to Quantify Mineral Re-crystallization From Granulite to Eclogite Metamorphism in the Western Italian Alps (Sesia-Lanzo Zone)

et al. 2019

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“…This method is useful to quantitatively extrapolate the sequence of the metamorphic assemblages related to the different fabrics (e.g., [5,19,86]), as well as to investigate the potential mineral zoning pattern within a single mineral phase and/or alongside the border of two mineral phases. Q-XRMA is an image processing tool package based on several image analysis functions written in Python and largely based on the ArcGIS ® library functions, in the same line of several tools progressively developed to address different geosciences-related issues (e.g., [5,[14][15][16][86][87][88][89][90][91]).…”

Section: Methodsmentioning

confidence: 99%

Quantitative X-ray Maps Analaysis of Composition and Microstructure of Permian High-Temperature Relicts in Acidic Rocks from the Sesia-Lanzo Zone Eclogitic Continental Crust, Western Alps

et al. 2021

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Three samples of meta-acidic rocks with pre-Alpine metamorphic relicts from the Sesia-Lanzo Zone eclogitic continental crust were investigated using stepwise controlled elemental maps by means of the Quantitative X-ray Maps Analyzer (Q-XRMA). Samples were chosen with the aim of analysing the reacting zones along the boundaries between the pre-Alpine and Alpine mineral phases, which developed in low chemically reactive systems. The quantitative data treatment of the X-ray images was based on a former multivariate statistical analytical stage followed by a sequential phase and sub-phase classification and permitted to isolate and to quantitatively investigate the local paragenetic equilibria. The parageneses thus observed were interpreted as related to the pre-Alpine metamorphic or magmatic stages as well as to local Alpine re-equilibrations. On the basis of electron microprobe analysis, specific compositional ranges were defined in micro-domains of the relict and new paragenetic equilibria. In this way calibrated compositional maps were obtained and used to contour different types of reacting boundaries between adjacent solid solution phases. The pre-Alpine and Alpine mineral parageneses thus obtained allowed to perform geothermobarometry on a statistically meaningful and reliable dataset. In general, metamorphic temperatures cluster at 600–700 ∘C and 450–550 ∘C, with lower temperatures referred to a retrograde metamorphic re-equilibration. In all the cases described, pre-Alpine parageneses were overprinted by an Alpine metamorphic mineral assemblage. Pressure-temperature estimates of the Alpine stage averagely range between 420 to 550 ∘C and 12 to 16.5 kbar. The PT constraints permitted to better define the pre-Alpine metamorphic scenario of the western Austroalpine sectors, as well as to better understand the influence of the pre-Alpine metamorphic inheritance on the forthcoming Alpine tectonic evolution.

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“…Based on their unique color, pores can be identified by threshold methods in the RGB or HSV color spaces [5,6]. In addition, pattern recognition and GIS-based methods are applied to extract the boundary and region of the pore as a polygon object, and, further, to quantitatively calculate its shape, orientation, type, and spatial distribution [7][8][9][10]. Moreover, the deep-learning methods classify the thin-section image pixel by pixel, which is called image semantic segmentation, creating the labeled output image, where every single labeled pixel represents a mineral class or pore [11,12].…”

Section: A Pore Information Extractionmentioning

confidence: 99%

Rock classification in petrographic thin section images based on concatenated convolutional neural networks

Zhu

et al. 2020

Earth Sci Inform

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Rock classification plays an important role in rock mechanics, petrology, mining engineering, magmatic processes, and numerous other fields pertaining to geosciences. This study proposes a concatenated convolutional neural network (Con-CNN) method for classifying the geologic rock type based on petrographic thin sections. Herein, plane polarized light (PPL) and crossed polarized light (XPL) were used to acquire thin section images as the fundamental data. After conducting the necessary pre-processing analyses, the PPL and XPL images as well as their comprehensive image (CI) were incorporated in three convolutional neural networks (CNNs) comprising the same structure for achieving a preliminary classification; these images were developed by employing the fused principal component analysis (PCA). Subsequently, the results of the CNNs were concatenated by using the maximum likelihood detection to obtain a comprehensive classification result. Finally, a statistical revision was applied to fix the misclassification due to the proportion difference of minerals that were similar in appearance. In this study, 13 types of 92 rock samples, 196 petrographic thin sections, 588 images, and 63504 image patches were fabricated for the training and validation of the Con-CNN. The five-folds cross validation shows that the method proposed provides an overall accuracy of 89.97%, which facilitates the automation of rock classification in petrographic thin sections.

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Semi-automated procedure of digitalization and study of rock thin section porosity applying optical image analysis tools

Cited by 21 publications

References 42 publications

Integrating X-Ray Computed Tomography With Chemical Imaging to Quantify Mineral Re-crystallization From Granulite to Eclogite Metamorphism in the Western Italian Alps (Sesia-Lanzo Zone)

Integrating X-Ray Computed Tomography With Chemical Imaging to Quantify Mineral Re-crystallization From Granulite to Eclogite Metamorphism in the Western Italian Alps (Sesia-Lanzo Zone)

Quantitative X-ray Maps Analaysis of Composition and Microstructure of Permian High-Temperature Relicts in Acidic Rocks from the Sesia-Lanzo Zone Eclogitic Continental Crust, Western Alps

Rock classification in petrographic thin section images based on concatenated convolutional neural networks

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