Grain segmentation in sandstone thin-section based on computer analysis of microscopic images

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PAPER • OPEN ACCESS

Grain segmentation in sandstone thin-section based on computer analysis of microscopic images

To cite this article: Przemyslaw Dabek et al 2023 IOP Conf. Ser.: Earth Environ. Sci. 1189 012026

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Grain segmentation in sandstone thin-section based on computer analysis of microscopic images

Przemyslaw Dabek¹, Krzysztof Chudy¹, Izabella Nowak², Radoslaw Zimroz¹

1Faculty of Geoengineering, Mining and Geology, Wroc law University of Science and Technology, Poland

2KGHM Cuprum Research and Development Centre, Poland E-mail: [email protected]

Abstract. The segmentation of minerals visible in sandstone thin-sections is a necessary part of microscopic estimation of mineral composition, the most important part of petrographic and sedimentological studies. This process is often a hard task due to the difficulty in determining the exact boundaries between grains, mainly caused by secondary alteration in minerals, etching of grains and pore-space filling by cements. Structural features play a very important role in mineral identification and undoubtedly without their use mineral recognition in thin sections gives many misclassification results. Calculation of each grain area is an important part of the process that is very time-consuming if done by-hand. Presented method provides a precise solution while mixing automated and non-automated approach. Photos of a sandstone thin section were taken using a Nikon Eclipse LV100N POL polarizing microscope, at 200x magnification, in transmitted light, with crossed polarizers. Then to determine the borders and therefore area of distinct grains, a sample image has been chosen. Initial contours have been created by-hand inside graphic application on a tablet device, as the non-automated part of the presented method. Afterward, the layer with marked boundaries was analyzed by computer software. In the automated part of the method, marked contours of grains were detected inside the algorithm with areas calculated afterward.

Keywords: Mineral segmentation, petrography, image analysis, edge detection

1. Introduction

Mineral recognition and classification are one of first tasks for a geologist. This process begins during fieldwork when a rock in microscopic scale is described in rock outcrop or extracted from the earth depth as drill core. Rock classification is detailed during laboratory study.

Rock macroscopic description is based on identification of distinctive minerals it consists of, their size, shape and arrangement of mineral grains in the rock space plays an important role in many fields of geology, such as sedimentology, petrology, deposit geology, rock mechanics, mining engineering as well as outside of geology, such as construction and material engineering [1, 2, 3, 4, 5]. In macro scale, the particle-size distribution can help with evaluation of deposit quality [6] or blasting process optimization [7].

The basic classification of the rock depends on qualitative and quantitative analysis of the rock-forming minerals [8]. This analysis is performed using a range of research methods. They are complemented by polarized light optical microscopy, which is a reference point for using

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the services of more advanced technicians, e.g. electron microscopy (SEM, TEM), microarea chemical analysis (EPMA, SEM-EDS) or x-ray diffraction analysis (XRD) [9]. To carry out a microscopic examination of rocks and minerals from rock sample, a microscopic preparation is made in the form of the polished plate, with thickness of 30-50 microns that allows observation of minerals in polarized light [8, 9, 10]. Different optical properties of minerals allow for their identification and obtaining information about the fabric of all components in the rock, as well as the determination of the total amount of particular minerals in the tested sample.

One of the important tasks of geology is the characterization of reservoir parameters of rocks that build the layers in which water, oil, or gas accumulate. An important element is to designate of the total porosity and open porosity, which determine the possibility of fluids migration and accumulation within the rock medium [11, 12].

These are the basic parameters taken into account when defining the resources of a given raw material that can be extracted to the surface. To determine the described parameters, the quantitative analysis of porosity in the rock is used, which can also be performed with an optical microscope based on at least a dozen (preferably a couple dozens) samples prepared in the form of thin sections. Determining the quantitative composition of minerals and rock pores in a thin plate under the microscope (expressed in percents by volume) is normally done by manually counting points (modular method), which is of intermediate accuracy and has a drawback of inaccurate determination of skewness and curtosis [13]. The following methods can be mentioned: planimetric, linear, or point.

In the modular method, the number of planimetric grid points covering the entire surface of the thin-section or its selected part is manually counted, falling on previously identified elements.

In the case of sandstones, these are framework grains, matrix and cement, as well as pore space.

(Fig. 1). The data is compiled in a table, and then the percentage share of individual elements for the analyzed area is calculated. Without the use of automation and computer analysis, it is a laborious and lengthy process. Taking measurements for one analyzed area, depending on the complexity of the rock structure and the adopted measurement grid takes from 3 to 8 hours. The density of the measurement grid is selected based on the size of the grains and the distances between them. It should be noted here that the less dense the measuring grid, the bigger measurement error can be expected. Additionaly, potential error may vary significantly based on the operator experience [14].

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Figure 1. Photograph of a part of a thin section of sandstone under an optical microscope, under transmitted light with crossed polarizers, on which a planimetric grid was plotted

The second important element in thin section descriptions is the mineral composition of the reservoir rocks. Individual minerals, their forms of occurrence, inclusions, and the of boundaries between mineral grains are identified [15, 16]. These features are characteristic of rocks occurring in a given area (specific geological space) and are related to the process of formation of reservoir rocks. As a result of weathering and erosion, the primary rocks (magmatic, metamorphic and also sedimentary rocks) were subjected to disintegration into smaller elements, which in turn were transported to the sites of final deposition. In these places, they underwent diagenetic processes, creating more or less favorable conditions for the accumulation of liquid and gaseous materials.

A well-conducted analysis of the mineral composition of reservoir rocks makes it possible to describe geological processes for a given area and allows for a comparative analysis between distant reservoir structures. Such an analysis is important at the stage of searching for new crude oil and natural gas resources. This also makes it possible to extrapolate the results results from well-documented geological structures to areas at the early stages of exploration, for which we find analogies to processes shaping them.

Automation of quantitative measurements in sandstones is a topic of interest to many researchers (including [4, 17, 18, 19]). Striving to eliminate tedious and time-consuming counting

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of points and obtaining a large number of repeatable results in a relatively short time, manual work to obtain the quantitative mineral composition of a rock sample is supported by the use of mechanical measuring devices, e.g. computer-controlled mechanical tables and methods based on computer analysis of the microscopic image [9].

In this article, we present a method of improving the measurement process using machine vision, end result of which will be semi-automatic recognition (segmentation) of a thin-section into individual fields with a proper assignment (mineral grains, matrix, cement). The use of computer image analysis in the conducted research accelerates the processing time of research material. The process of preparing a thin-section for microscopic analysis remains unchanged.

The qualitative modification takes place at further stages of the work.

An automated method for minerals segmentation in thin section has been discussed in many papers. One example could be a solution based on incremental color clustering where authors used 12 color features with end results ranging respectively from 92% overall accuracy in sections without altered minerals to 85% in sections with altered minerals [20]. As usual in tasks of visual data recognition and segmentation, artificial neural networks (ANNs) were used and tested with different textural features extracted beforehand ([21, 22]).

2. Experiment description

The initial stage of the research is to prepare a thin section from the collected sandstone sample.

Preparing a good-quality sandstone thin section is an exacting process. First, a smaller cuboid- shaped chip of sandstone rock had been cut from a section of sandstone drill core (Fig. 2 A, B). Then a thin slice of rock was cut using a diamond saw to fit on a standard petrographic microscope slide (typically 28 x 48 mm). This slice of the rock was glued to the glass slide using epoxy (Fig. 2 C) and then it were ground (Fig. 2 D) until it’s optically flat using progressively finer abrasive grit. This means that light actually passes through the slice of rock sliver of material. The standard thickness of for a thin section for an optical microscope study is 30 microns. Finally, the coverslip is glued onto the surface of the thin section.

When observing sandstone thin section in optical microscope under polarized light, optical features of minerals including pleochroism, light extinction, cleavage, interference colors are visible. Because individual minerals vary in their optical properties, most rock-forming minerals can be easily identified by polarized light in optical microscopy.

Photos of a sandstone thin section were taken using a Nikon Eclipse LV100N POL polarizing microscope, at 200x magnification, in transmitted light, with crossed polarizers. All the rock forming minerals have been identified. A series of images were taken every 10 degrees of rotation of the microscopic stage. 10 images were obtained for the given study area.

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Figure 2. Steps in preparation of rock (sandstone) thin section A – samples of a drill core, B - cuboid-shaped chips of rock, C - a smaller slice of rock in a cuboid shape mounted in a glass-slide, D – sandstone thin section ready to be polished, E – final sandstone thin section

Then, in order to determine the borders and therefore area of distinct grains, a sample image has been chosen. Initial contours have been created by-hand inside graphic application on a tablet device, as the non-automated part of the presented method. The device used for this experiment was Galaxy Tab S6 Lite and used graphic application was IbisPaint X, which is entirely free to use. Any graphic application can be used if it provides option of painting and exporting another layer over the image or other way of providing borders for automatic area detection and calculation.

3. Methodology

Presented methodology focus on obtaining the area of each distinctive part of the analyzed mineral. Proposed solution to the problem can be described in two consecutive steps, which can be seen in Fig. 3.

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Figure 3. Flow-chart of the algorithm

First, the correct contours have to be detected based on the potential changes in the series of the images taken under different light angle. Such a decision cannot be taken under the analysis of a single image, because some reflection properties can create the situation, where two different but positioned next to each other parts of the mineral could present themselves as a single area.

In the presented methodology this problem is solved in a non-automated way where human is responsible for both detection and drawing the correct lines to distinguish different parts of the mineral. The simplest way is to use any graphic software that allows for the creation of additional layers over the existing image. After drawing the lines on the second layer, it can be then saved as a separate image and carried over to the next part of the algorithm.

After that, the image is analyzed to extract the information about the area of previously distinguished parts of the image.To achieve that, an edge detection algorithm is used in the first place. Since the edges created by the lines are clearly visible on the white background, any edge algorithm can be used as long as it allows for detection in both horizontal and vertical direction.

Canny or Sobel can be taken as examples, although the first one was used in the case of the results presented in Fig. 4.

After that, some additional pre-processing has to be applied. Operation of morphological

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number can be then recalculated into more meaningful units, based on the physical dimensions of the part of the mineral on the analyzed image. In this case percentage of the image area gives the information required to finalize the analysis.

4. Results

The test image used for the algorithm presentation can be seen in Fig. 4. It has been used as a base for hand-drawing the contours and later comparison as if they have been detected correctly by the contour detection algorithm.

Figure 4. Image of the mineral

The effect of the hand-drawing can be seen in Fig 5. This is the image that serves as a base for automatic contour detection. It was exported as the second layer that was drawn over one of the images with contours based on the information coming from the whole set.

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Figure 5. Image obtained from the hand-drawn contours

Obtained image (Fig. 5) has been afterward processed by the simple image analysis algorithms. That consist of applied edge detection and morphological closing needed due to holes and line breaks created during the hand-drawing process, which can be expected. The result in form of blue lines (created for validation) as contours detected afterward can be seen in Fig. 6. Very small areas between expected contours were omitted by stating minimal area size relative to the image size.

Figure 6. Contour detection result

The last step was to calculate the area of each distinctive contour and display it on the image.

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Figure 7. Contour detection result 5. Conclusions

A sample photo of sandstone taken with an optical microscope was analyzed in order to extract the boundaries of the individual elements of the rock. The process has been streamlined through automatic area calculation, which in the first place uses a graphic application to draw the borders by the user, that afterward are detected and analyzed by the computer algorithm. The result of measuring the surface of a given element (in the form of a summary table with a supporting picture) is obtained near instantly after providing the prepared data. The final stage of the work is the grouping of individual separated areas based previously conducted tests of the qualitative mineral composition This procedure, in addition to increasing the precision of measurements, speeds up the process and allows for significant reduction of work-time.

The advantage of the presented method is also the fact that the proposed method of quantitative analysis does not require continuous access to a polarizing optical microscope integrated with a digital camera, access to which is sometimes limited. The microscopic photo of the rock together with the qualitative description of the mineral composition can be further analyzed on any computer or tablet that is a commonly available device. Additionally, by changing the calculated parameters, the sample (photo) can be analyzed depending on the context of the research. The completed photo documentation can be stored in the form of a graphic file and is not subject to the quality degradation process.

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