共查询到20条相似文献，搜索用时 31 毫秒
1.
连续消光是指在正交偏光显微镜下,岩石薄片随载物台旋转而发生消光强弱连续变化的现象。作为人工鉴定岩石薄片的重要依据,提取后的连续消光特征可以用于实现岩石薄片的自动分析。结合数字图像处理技术与聚类划分算法,开发出一套能够分割出孔隙、颗粒轮廓,实现颗粒类别划分,并对结果做定量分析的岩石薄片分析系统。从矿物的消光特征本质出发,连续消光特征提取精确与否是能否有效划分颗粒类别的关键环节。因此,针对系统在连续消光特征提取过程中的消光位对齐与消光基值去除操作分别设置了对照组。以鄂尔多斯盆地某区长6段砂岩薄片在正交偏光镜下的角度域序列图像进行实验,结果表明,所开发的基于岩石薄片分析系统能够提取到更为精确的连续消光特征,且该系统定量分析结果与专业人员鉴定结果基本一致。 相似文献
2.
This paper presents the development and utilisation of an automated image processing algorithm for detection and analysis
of grains. Using optical polarising microscopy, a set of colored images are collected from an area on a thin section. A filtering
operation, using rotation of a morphological alternating sequence filter (based on a structuring element), is used to remove
twinning features within individual grains. Filtering is followed by the watershed segmentation technique to determine grain
boundaries. The method is used for the identification of calcite grains in marble and the subsequent analysis of morphological
anisotropy. 相似文献
3.
通过将砂样图像进行单颗粒分割,识别砂样成分,可显著提高砂样岩性分析的准确性和效率。现有的砂样图像分割方法主要以传统分水岭算法和卷积神经网络为主,但由于对单颗粒岩屑轮廓细节提取不足,误分割率高。本文提出一种以图像融合算法为桥梁,将卷积神经网络和分水岭算法相结合的单颗粒图像分割提取方法。首先利用改进的Mask R-CNN网络快速分割砂样原图,获得其初分割图像;然后,将初分割图像与砂样原图进行融合,再使用改进的分水岭算法对融合结果进行分割;最后,利用砂样原图坐标点匹配方法,将分水岭分割得到的结果图像进行修正,完成单颗粒岩屑图像提取。实验结果表明,本文的单颗粒自动分割提取方法准确率高达96.77%,且模型更轻量和精准,为岩屑图像分割提供了一种可行且有效的方法,可满足有效测算油藏层构造变化、查找潜在沉积物源及储层动态变化的需求。 相似文献
4.
5.
针对数字全景钻孔摄像系统获取的实测图像,提出了一种基于颜色特征的数字式钻孔图像溶隙结构识别方法。利用岩层中的典型结构,如土质层、溶隙在颜色上与普通岩石具有较大差异性的特点,首先建立了一个自适应HSV颜色空间溶隙结构检测模型,利用该模型获取溶隙结构的二值化图像;对该二值化图像进行滤波处理;然后从处理后的二值化图像进行分区像素密度统计来确定土质层或溶隙区域的深度、面积及方位角等信息,从而实现数字式钻孔图像中溶隙结构的自动识别。通过对大量数字式钻孔图像进行试验并与对应的钻孔雷达图像进行结果对比表明,其方法能对全孔图像的溶隙和土质层进行快速、准确地自动化检测与定位,为钻孔图像岩体结构的自动识别与工程应用提供了一种新的可靠方法。 相似文献
6.
在岩石薄片正交偏光显微镜下角度域序列图像采集的基础上,分析了不同岩石组分在消光角度域上的光学特征及其差异性,并据此提出一种新的岩石颗粒分割和孔隙分析的方法。充分利用岩石颗粒赋存状态及其晶体光轴产状的复杂性、岩石颗粒空间排列及其接触关系的多样性,对岩石薄片在消光角维度上进行像素尺度的相关分析,并提出相关系数均值、相关系数标准差以及相关系数均差比等敏感参数,实现了岩石颗粒的分割和孔隙的提取。研究表明岩石颗粒内部的像素点灰度和RGB值在角度域上的相关性较强,在颗粒边缘及孔隙内部填隙物分布区域,其相关系数较低,且相关系数标准差要明显高于岩石颗粒内部。该方法从消光特征出发论证了角度域信息完整性的意义,提取的颗粒边缘较为清晰,孔隙结构骨架得以刻画,颗粒分割的效果好于Sobel和Canny等方法。 相似文献
7.
Marina Hruška William Corea Donald Seeburger William Schweller William H. Crane 《Mathematical Geosciences》2009,41(6):703-716
We describe a wavelet-transform-based method for automated segmentation of resistivity image logs that takes into account
the apparent dip in the data and addresses the problem of discriminating lithofacies boundaries from noise and intrafacies
variations. Our method can be applied to borehole measurements in general, but might have an advantage when applied to resistivity
image logs as it addresses explicitly the large variability in facies segments recorded with a high-resolution multiple-sensor
tool. We have developed an algorithm based on this method that might outperform other existing segmentation methods in the
cases of low to moderate dip. We made a detailed comparison of the segmentation from our method with the one done by a geologist
to delineate different lithofacies blocks in a well drilled in a deepwater depositional environment. Our results show considerable
success rates in reproducing the geologically defined lithofacies boundaries, and the generality of our procedure suggests
it could also be applied to other depositional environments. 相似文献
8.
Yuzhu Wang Christoph H. Arns Sheik S. Rahman Ji-Youn Arns 《Mathematical Geosciences》2018,50(7):781-799
The three-dimensional high-resolution imaging of rock samples is the basis for pore-scale characterization of reservoirs. Micro X-ray computed tomography (µ-CT) is considered the most direct means of obtaining the three-dimensional inner structure of porous media without deconstruction. The micrometer resolution of µ-CT, however, limits its application in the detection of small structures such as nanochannels, which are critical for fluid transportation. An effective strategy for solving this problem is applying numerical reconstruction methods to improve the resolution of the µ-CT images. In this paper, a convolutional neural network reconstruction method is introduced to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high-resolution scanning electron microscope (SEM) images. The proposed method involves four steps. First, a three-dimensional low-resolution tomographic image of a rock sample is obtained by µ-CT scanning. Next, one or more sections in the rock sample are selected for scanning by SEM to obtain high-resolution two-dimensional images. The high-resolution segmented SEM images and their corresponding low-resolution µ-CT slices are then applied to train a convolutional neural network (CNN) model. Finally, the trained CNN model is used to reconstruct the entire low-resolution three-dimensional µ-CT image. Because the SEM images are segmented and have a higher resolution than the µ-CT image, this algorithm integrates the super-resolution and segmentation processes. The input data are low-resolution µ-CT images, and the output data are high-resolution segmented porous structures. The experimental results show that the proposed method can achieve state-of-the-art performance. 相似文献
9.
Identification of Mineral Grains in a Petrographic Thin Section Using Phi- and Max-Images 总被引：1，自引：0，他引：1
A new approach to identifying grains in a petrographic thin section is presented in this paper. The mineral grain boundaries are detected using two synthetic images, created by mapping the maximum birefringence color intensity (max-image) and the corresponding angular rotation at which it occurs (phi-image), instead of original images obtained by rotating the section between crossed polarizers. Edge detection and image segmentation operations are first applied on the phi- and max-images separately. The two segmented images resulting from edge detection are then combined to generate a new segmented image, which preserves edges with higher reliabilities and eliminates those with lower reliabilities in the two former segmented images. The identification rate is thereby greatly improved. The method has been implemented in C++ in the Linux environment. Two sets of images are used to test the method. Each set has 200 images corresponding to 200 rotation angles between 0 and 180°. 相似文献
10.
11.
L. I. Kuncheva J. J. Charles N. Miles A. Collins B. Wells I. S. Lim 《Mathematical Geosciences》2008,40(6):639-652
We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty. 相似文献
12.
近些年,定量化火成岩结构研究表明,利用常规的岩矿鉴定设备,获取不同尺度的火成岩二维岩相学照片,通过肉眼识别矿物颗粒,并借助图像处理和结构分析软件,可以准确地量化火成岩的结构特征。本文结合近些年国内外同行的研究成果,对火成岩二维定量化结构分析方法中常用的多种观测方式优缺点进行了总结。粒度在毫米级以下的火成岩的定量化结构参数,可以用偏光显微镜下的透射光、反射光、阴极发光和电子探针背散射成像中的两种或两种以上观测方式进行分析,并具有较高的精度和准确度。粒度小于0.03 mm的各种镁铁质矿物可用反射光和背散射图进行分析,灰度近似的镁铁质矿物可以利用图像处理软件赋予不同的彩色,提高颗粒间的辨识度。常规偏光显微镜下不易区分的长英质矿物和多数副矿物可用偏光显微镜阴极发光进行分析。粒度在毫米级以上的造岩矿物可以用光片或野外测量的方式进行定量分析。为了方便相关领域学者使用火成岩二维定量化结构分析方法,本文详细列出了具体的分析步骤,并结合一个玄武岩样品中的橄榄石斑晶数据结果,重点分析以下4个方面的问题:(1)如何准确识别矿物颗粒边界;(2)矿物含量和形态的确定;(3)分析区域面积和颗粒数的确定;(4)不同晶体群的区分。分析结果表明,颗粒数100~500颗时,晶体粒度分布(CSD)的截距和斜率、矿物含量、定向程度和粒状矿物的三轴比在误差范围内没有显著区别,但颗粒最大长度和聚集程度会被低估。当颗粒数小于300颗时,晶体空间聚集程度的R值会被高估0.05~0.2,这一点在以往的研究中没有得到充分重视。当颗粒数大于500颗时,所有结构参数都趋于稳定,且精度和准确度都会显著提高。目前多数研究者提供的结构参数往往与观测和统计方式有关,缺乏对应的原始数据,不方便同行间的对比研究,建议学者今后发表相关成果时,提供详细的分析步骤和最原始的数据。分析步骤重点说明包括:(1)聚集矿物边界的识别和处理方式;(2)晶体三维形态的确定方法,样品间CSD参数的变化是否是由形态参数变化引起;(3)能够准确识别的矿物颗粒最小粒度;(4)利用颗粒数较多的样品选取较小的不同区域重复分析3到5个不同区域,评估样品的均一性,并据此估计样品的分析精度。原始数据方面包括:(1)提供同一个样品至少一个不同区域的分析结果,如果是多个作者的研究成果,建议提供至少两人独立分析的结果用来评估数据的精度和准确度;(2)文章正文或附件中应该提供每个样品不同粒度间隔的颗粒数,样品原始的高分辨率矿物轮廓描绘图或图片分析的相关原始参数。火成岩出现复杂晶体群时,定量化的结构参数往往体现的是多种晶体群的混合特征,并且与不同晶体群的比例有关。未来的研究需要结合多种观测方式和微区成分分析重点识别不同晶体群的结构参数,对粒度和成分近似的多种晶体群的识别,还需要开发更多有效的方法,这对准确认识火成岩结构多样性的成因和岩浆作用过程都有重要意义。 相似文献
13.
Deformation Field around the Stress Induced Crack Area in Sandstone by the Digital Speckle Correlation Method 总被引：1，自引：0，他引：1
Abstract: The deformation field around sub-cracks was calculated using the digital speckle correlation method. First, the uni-axial compression tests on sandstone samples containing a pre-fabricated fracture were made. Photomicrographs showing the characteristics of the sub-crack development were taken using a scanning electron microscope (SEM). From these photomicrographs, the real-time images showing the initiation, growth and coalescence of sub-cracks and micro-cracks in the sandstone specimens were obtained and the effects of loading level as well as grain boundaries on the development of cracks were analyzed. Second, the intensity images of the sandstone specimen surface were captured from the observations of the SEM corresponding to different loading levels. Then correlation computation was carried out for the sequential pairs of intensity images to evaluate the displacement components, as well as the strain field. The results show that the deformation varies in different areas separated by sub-cracks during rock damage processes. 相似文献
14.
The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face. 相似文献
15.
利用扫描电镜的背散射电子图象(BEI)对玄武岩及金伯利岩所含幔源包体中橄榄石的缀饰位错进行了研究。实验结果表明,位错类型和颗粒边界图象清晰,又便于观察。这种观察位错的方法其分辨率比光学显微镜观察结果约高一个数量级。背散射电子图象位错方法特别适用于研究天然的和实验变形橄榄石的高位错密度和密集型边界,对于确定显微构造的定量参数也十分有利。 相似文献
16.
针对岩石颗粒边缘模糊、结构复杂的特点,为了更有效地识别颗粒边缘,在基于特征值的C3相干算法的基础上,融合多尺度和多角度的特征表达,提出了一种改进的C3相干算法。该算法综合考虑岩石薄片图像角度域光学特征、空间尺度信息和各向异性信息,能更有效地表征颗粒边缘特征,表现出对复杂矿物结构的适应能力。在采集的岩石薄片正交偏光图像上验证提出的算法,实验结果表明,与原生C3相干算法相比,改进后的C3相干算法在全局图像上的方差和灰度差分乘积分别提升了68.41%和22.91%,信息熵下降了21.61%。 相似文献
17.
The continuous improvement of the launched satellites’ spatial and spectral resolutions has brought new challenges for remote sensing image segmentation technology. The traditional supervised methods greatly depend on artificial interpretation and reduce the degree of automation and robustness of image segmentation. Therefore, the article proposes a novel unsupervised multi-scale segmentation method for high-resolution remote sensing images based on automated parameterization and it mainly includes three steps, adaptive selection of scale parameter (SP) based on local area homogeneity index J-value, multi-scale segmentation based on the inter-scales boundaries constraint strategy, and region merging based on multi-features. The article makes experiments by multi-group high-resolution remote sensing images of different launched satellites and compares the proposed method with the well-known commercial software eCognition and a traditional supervised method. The results show that the proposed method can locate the object edges more accurately and extract the object outlines more completely, and needs no human intervention in segmentation process, so it can provide a generic and effective unsupervised solution for high-resolution remote sensing image segmentation. 相似文献
18.
A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing 总被引：2，自引：2，他引：0
Mir Amid Hashemi Ghonwa Khaddour Bertrand François Thierry J. Massart Simon Salager 《Acta Geotechnica》2014,9(5):831-846
X-ray computed tomography is a powerful non-destructive technique used in many domains to obtain the three-dimensional representation of objects, starting from the reconstitution of two-dimensional images of radiographic scanning. This technique is now able to analyze objects within a few micron resolutions. Consequently, X-ray microcomputed tomography opens perspectives for the analysis of the fabric of multiphase geomaterials such as soils, concretes, rocks and ceramics. To be able to characterize the spatial distribution of the different phases in such complex and disordered materials, automated phase recognition has to be implemented through image segmentation. A crucial difficulty in segmenting images lies in the presence of noise in the obtained tomographic representation, making it difficult to assign a specific phase to each voxel of the image. In the present study, simultaneous region growing is used to reconstitute the three-dimensional segmented image of granular materials. First, based on a set of expected phases in the image, regions where specific phases are sure to be present are identified, leaving uncertain regions of the image unidentified. Subsequently, the identified regions are grown until growing phases meet each other with vanishing unidentified regions. The method requires a limited number of manual parameters that are easily determined. The developed method is illustrated based on three applications on granular materials, comparing the phase volume fractions obtained by segmentation with macroscopic data. It is demonstrated that the algorithm rapidly converges and fills the image after a few iterations. 相似文献
19.
This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples.
The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented
kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust
the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying
out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite
and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data
for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap
d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the
robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value
of d=0.5 (89.07%). 相似文献
20.
Crystal size distributions (CSDs) are a standard method of describing populations of crystals within magmatic rocks. Olivine
is the dominant phase in kimberlite (∼40–50% by volume) and features a diverse range of sizes, shapes and origins. CSDs of
olivine provide a logical means of semi-quantitatively characterising kimberlite. The CSDs can then be used to distinguish
or correlate between kimberlite bodies or to investigate processes related to ascent, emplacement and eruption. In this paper,
we present an automatic image analysis technique that provides efficient quantification of olivine CSDs within digital images
of polished slabs of kimberlite. This technique relies on a combination of algorithms for detecting regions of interest (ROI)
and for segmentation of ROIs in order to identify individual olivine crystals that are used for size distribution datasets.
The detection process identifies regions expected to be olivine using a model-based colour detection technique using Mahalanobis
distance combined with texture analysis based on local standard deviation and greyscale foreground enhancement techniques.
The segmentation process separates adjacent domains to identify individual crystals using an iterative marker-based watershed
algorithm to separate adjoined structures of varying sizes. We demonstrate the utility of automatic image analysis by comparing
CSDs for olivine derived from this method versus results from manual digitisation of olivine grains. The automatic detection
system correctly identified ∼86% of the manually detected olivine domains; ∼88% of the automatically detected regions correctly
correlate to manually defined olivine grains. Discrepancies between the two methods are mostly the result of oversimplification
of crystal margins (i.e. rounding) by manual tracing whereas automatic boundary recognition shows clear advantages in identifying
irregularities in crystal edges. Closer examination of the results shows that both methods suffer from under-representation
of smaller crystals due to: (1) human subjectivity and error in manual tracing and (2) noise removal processes in automatic
detection. Automatic detection of olivine grains is much more efficient than conventional manual tracing; manual detection
requires ∼6 h per sample versus ∼1 min for automatic analysis of the same sample. 相似文献