Comparative Analysis of The Results of Studying Fabrics of Knidian Amphorae by Various Natural-Scientific Methods
0
Citation
5
Reference
10
Related Paper
Keywords:
Thin section
Traditional petrographic number (PN) ratings of concrete aggregates cannot work reliably in Florida because most of the state’s rocks are too fine-grained for use of macroscopic petrographic evaluations. This study designed a thin-section approach to PN calculations, based on relevant carbonate rock components that can be seen easily in thin section. The primary lithologic features chosen to differentiate the carbonates are the types of allochems, the porosity type, the kind of cement or matrix, and any additional noncarbonate minerals. Ten sets of aggregate samples were examined (eight carbonate and two noncarbonate sets) to test the method and ranking of thin-section PN determination. Prior work with PN values showed that aggregates with PN values of less than 140 yield good field performance, 140 to 160 have fair to poor field performance, and those greater than 160 normally have poor performance. Results of the thin-section PN method found values for our samples ranging from 100 (an ideal aggregate) to 149. By using factor weights (FWs) developed, six aggregates have PNs below 140 (good), and four scored between 140 and 160 (fair to poor) and may need additional testing. This method of PN calculation is easy to learn, clearly focused on carbonate aggregates like those in Florida and surrounding regions, and relatively inexpansive, and with it, soundness predictions can be determined quickly (in approximately 1 h per sample). Correlation of thin-section PN values with field performance information has not yet been completed, however, and this must be done before predicted performance can be considered reliable.
Thin section
Lithology
Soundness
Section (typography)
Cite
Citations (2)
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.
Thin section
Section (typography)
Contextual image classification
Cite
Citations (1)
By using the HF—HCl—HNO3 system with high-pressure closed digestion,which can effectively dissolved cobalt-rich crust samples,fifty major and minor elements in cobalt-rich crusts can be determined by means of inductively coupled plasma Optical Emission spectrometry(ICP-OES)and inductively coupled plasma mass spectrometry(ICP-MS).The detection limit of this method is 2～40μg·g-1 by using ICP-OES and 6～80ng·g-1 by using ICP-MS.The validation of the method with cobalt-rich crust references GBW07337,GBW07338and GBW07339shows that both the relative standard deviation(RSD)and the relative error(RE)are less than 5%.Applying to the determination of cobalt-rich crust samples collected from the China Pacific Area,the precision(RSD,n=6)can be less than 5%and the recoveries are 90.0%～108%.Thus,this method can be applicable to the analysis of a large number of cobalt-rich crust samples.
Cite
Citations (1)
Thin section
Section (typography)
Contextual image classification
Cite
Citations (52)
Thin section
Cite
Citations (0)
A method for The determination of Al2O3,and 5 elements Al.As.Mn.Hg.Pb.Cd in poly aluminium chloride by inductively coupled plasma mass spectrometer(ICP-MS)has been established. The contents of Al2O3 and These 5 elements in poly aluminium chloride could be simultaneously determined by ICB-MS.
Aluminium chloride
Cite
Citations (0)
Inductively coupled plasma-mass spectrometry(ICP-MS) was applied widely in elemental analysis for foods samples in advantage of the merits of low limit detection and simultaneous determination for more elements.With the development of the analytic technique,and as physical,chemical,nutritional and poisonous properties varies obviously due to the concrete elemental speciation,speciation analysis for elements was attracted attention of most analysts.ICP-MS determination coupled with all kinds of separation techniques was improved rapidly in elemental speciation analysis application for foods,including arsenic,selenium,tin,etc.This article simply summarized the domestic and foreign development and application of elemental speciation analysis for foods using ICP-MS related hyphenated technique.
Genetic algorithm
Cite
Citations (0)
Shales represent about one-half of all sedimentary rocks in the stratigraphic column. They consist mainly of clay minerals, quartz, feldspars, and micas, with minor amounts of other minerals such as carbonates, iron oxides, zeolites, and sulfates. Thus, they are mineralogically and chemically more complex than are either sandstones or limestones. Owing to their fine grain size, only a few petrographic studies of shales have been published. In thin section, many of the constituents of shales cannot be resolved optically because of their small size and the intermixing of clay minerals. Clay mineral flakes are so thin that several may be stacked irregularly upon one another so that light passing through is diffracted and/or refracted irregularly, producing an image with poor resolution. In addition, opaque materials such as hematite and amorphous inorganic and organic particles make petrographic viewing difficult (Blatt, 1982). Some workers have succeeded in using the petrographic microscope to study shales (e.g., Folk, 1960, 1962), but the process is time consuming and requires considerable expertise. The literature dealing with petrographic study of shales prior to 1980 is summarized by Potter et al. (1980). Recently, petrographic microscopy studies have been published by Schieber (1986, 1989, 1994), Odin (1988), Weaver (1989), and Leckie et al. (1990), O'Brien and Slatt (1990), and Bennett et al. (1991 a).
Thin section
Anhydrite
Cite
Citations (0)
Section (typography)
Thin section
Cite
Citations (19)
AbstractPetrophysical properties of petroleum reservoir rocks are usually obtained by laborious core laboratory measurements. The present study investigates the capability of petrographic image analysis applied on thin sections of reservoir rock and fuzzy logic for predicting porosity in carbonate rocks. The proposed methodology comprises two steps: first, the petrographic parameters, including porosity type, grain size, mean geometrical shape coefficient of grains, and texture type, were extracted for each thin section based on image analysis techniques. Consequently, the petrographic parameters were formulated to core porosity using a Takagi and Sugeno fuzzy inference system. Petrographic image analysis is an emerging technology, which provides fast and accurate quantitative evaluation from reservoir rock. The results of single petrographic image analysis showed inaccurate estimation of total porosity in all rocks except those that have an extremely isotropic pore structure. A quantitative evaluation of thin section images and fuzzy model was successfully used to improve the accuracy of porosity prediction and the results of thin section analysis were generalized to core plug analysis. The mean square error and correlation coefficient between two-dimensional measurements and core plug were obtained at 0.0262 and 86.3, respectively, which shows acceptable prediction of three-dimensional porosity from two-dimensional thin sections. Therefore, the results confirmed the validity of the propounded methodology.Keywords:: core laboratory measurementfuzzy logicpetrographic image analysisporosityreservoir rock ACKNOWLEDGMENTSThe authors would like to extend their appreciation to the Iranian Central Oil Fields Company for providing technical support during this research. They also are grateful to Dr. Amir Hatampour from the petrophysics department of Pars Oil and Gas Company (POGC) for helping during the research.FIGURE 3 (a) Crossplot showing correlation coefficients between fuzzy predicted porosity and measured core porosity. (b) A comparison between measured and fuzzy predicted porosity versus depth in test well.Display full size
Thin section
Petrophysics
Cite
Citations (6)