Comparing three image processing algorithms to estimate the grain-size distribution of porous rocks from binary 2d images and sensitivity analysis of the grain overlapping degree (original) (raw)
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Journal of Geological Research, 2014
Specific surface is an important parameter for predicting permeability of porous rocks. Many digital methods have been invented to extract the rock properties via imaging such as Micro-CT. With utilizing 3D volume data, this helps in precise investigation; however, it is neither economically efficient nor can be applied for different situations. In this study, a new approach is developed to estimate rock specific surface using 2D thin section images with micron resolution. One specific conclusion of this study is that there is specific ratio between the specific perimeter of 2D images and the specific surface in the 3D real rock structure. To further investigate this ratio several 3D blocks of rock volume data have been virtually cut in every possible angle and the value of specific perimeter calculated for each obtained 2D thin section. Finally, the predicted value of specific surface for 6 rock types is compared with the real values calculated from the original 3D data. Result indicates acceptable precision of this approach for sandstone rocks whereas not applicable for carbonate rocks.
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Computers & Geosciences, 2012
Geological images, such as photos and photomicrographs of rocks, are commonly used as supportive evidence to indicate geological processes. A limiting factor to quantifying images is the digitization process; therefore, image analysis has remained largely qualitative. ArcGIS s , the most widely used Geographic Information System (GIS) available, is capable of an array of functions including building models capable of digitizing images. We expanded upon a previously designed model built using Arc ModelBuilder s to quantify photomicrographs and scanned images of thin sections. In order to enhance grain boundary detection, but limit computer processing and hard drive space, we utilized a preprocessing image analysis technique such that only a single image is used in the digitizing model. Preprocessing allows the model to accurately digitize grain boundaries with fewer images and requires less user intervention by using batch processing in image analysis software and ArcCatalog s. We present case studies for five basic textural analyses using a semi-automated digitized image and quantified in ArcMap s. Grain Size Distributions, Shape Preferred Orientations, Weak phase connections (networking), and Nearest Neighbor statistics are presented in a simplified fashion for further analyses directly obtainable from the automated digitizing method. Finally, we discuss the ramifications for incorporating this method into geological image analyses.
IEE Proceedings - Vision, Image, and Signal Processing, 2005
Size distribution of rock fragments obtained from blasting and crushing in the mining industry has to be monitored for optimal control of a variety of processes before reaching the final grinding, milling and the froth flotation processes. Whenever feasible, mechanical sieving is the routine procedure to determine the cumulative rock weight distribution on conveyor belts or free falling off the end of transfer chutes. This process is tedious and very time consuming, even more so if a complete set of sieving meshes is used. A computer vision technique is proposed based on a series of segmentation, filtering and morphological operations specially designed to determine rock fragment sizes from digital images. The final step uses an area-based approach to estimate rock volumes. This segmentation technique was implemented and results of cumulative rock volume distributions obtained from this approach were compared to the mechanical fragment distributions. The technique yielded rock distribution curves which represents an alternative to the mechanical sieving distributions.
The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly.
Estimation of grain size distributions and associated parameters from digital images of sediment
Outlines an alternative approach to Rubin (2004) to estimate the distribution of grain sizes within an image of sediment. The method uses non-parametric kernel density functions on the vector of grain sizes per lag found through the least-squares solution of a sample image correlogram compared with a bank of calibration correlograms. Although not a direct solution to the distribution, the advantages of the approach include easily ascribing weightings to the vector of grain sizes, since the information within the correlogram may be a nonlinear function of lag size. Also the form of the distribution (e.g. normal, log-normal), if known for the sediment population under scrutiny, can be specified. Validation with beach gravel in the size range 1.4 and 32mm shows that mean and sorting are well approximated, but higher moments are poorly estimated. Finally, it is suggested that the correlogram should be estimated using a 2D fast Fourier transform approach, rather than the spatial approach of Rubin (2004), because it simultaneously maps energy at all wavelengths and directions. Due to likely anisotropy in images of sediment, a method is presented for estimation of upper and lower bounds on mean grain size using ellipse-fitting on the 2D correlogram.
A universal approximation of grain size from images of noncohesive sediment
A new method is proposed for estimating mean grain size from an image of sediment without the need for calibration, i.e. read directly from the image. This advance has the potential to make digital grain size considerably easier from a user perspective, and it opens up the possibility of grain size estimation in environments where the calibration procedure is difficult or impossible. The new method is found to give estimates to within 20% without calibration for sediment sizes over 3 orders of magnitude (from fine sands to cobbles). This error can be reduced to approximately 10% by correcting for population-specific bias, which is achieved in practice by carrying out point-counts of end-members of a sediment population. Numerical explanations are offered for the validity of the method. Finally, the new method is explored and tested using physical experiments and computer simulations of synthetic sediment beds.
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ANALYSIS OF ROCK FRAGMENTATION USING DIGITAL IMAGE PROCESSING
In this paper, a procedure for calculating the size distribution of rock fragments using video images is described. The procedure utilizes a high-resolution video camera for image capturing in the field and a set of computer algorithms for processing the video images. The computer program first delineates the individual rock fragments in the images. This is followed by statistical procedures that take into account fragment overlap and the two-dimensional nature of the images. The computer algorithms can process many images to produce a single size distribution curve, and takes into account sample variability as well as combining images taken at different scales. All the procedures described have been implemented into a single computer program. By comparing the computer procedures with laboratory experiments, the accuracy of the method is demonstrated.