火成岩结构的二维定量化分析方法

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。     

K0条件下颗粒形状特征对砂土剪切模量的影响

为了研究颗粒形状特征对砂土剪切模量的影响,选取4种形状不同的砂样（福建标准砂和人工石英砂）作为研究对象,通过扫描电镜获取其颗粒微观图像,使用Image Pro Plus软件和自编Matlab程序提取试样颗粒的形状特征。采用改进的半径角和轮廓指数综合描述颗粒偏离圆或椭圆的程度以及棱角变化程度,并基于这两个参数提出颗粒形状因子来综合表征颗粒的形状特征。之后在K0条件下通过弯曲元试验获得不同竖向荷载对应的4个试样的剪切模量,试验结果表明,砂土颗粒的形状因子与砂土的剪切模量具有良好的相关性。最后基于Hertz-Mindlin接触模型,推导出引入形状因子的砂土剪切模量理论计算公式,该计算公式与实测值吻合良好。     

Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method

X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.

     

法国GUERET花岗岩内黑云母的结构特征研究

应用形态数学、图象分析及立体学原理，通过岩石薄片研究，对Gueret花岗岩内黑云母的结构特征进行了定量分析，揭示了黑云母矿物粒子在花岗岩中的密度、粒径、形状及空间分布的规律。根据岩石薄片上黑云母的灰度特征，应用直接成象方法获得其灰度图象，用自动阈值方法可用来提取黑云母粒子从而构成二值图象。借助MIPS图象分析系统，可以评价花岗岩薄片样本图象中所有黑云母粒子的参数，包括面积、周长等。花岗岩内黑云母矿物的密度、粒径 形状及空间分布的分析，揭示了其各向异性特征；同时，威布尔、正态及泊橙模型可分别用来描述黑云母矿物的粒径、形状及空间分布特征。     

Refined modeling and movement characteristics analyses of irregularly shaped particles

Irregularly shaped (IRS) particles widely exist in many engineering and industrial fields. The macro physical and mechanical properties of the particle system are governed by the interaction between the particles in the system. The interaction between IRS particles is more complicated because of their complex geometric shape with extremely irregular and co‐existed concave and convex surfaces. These particles may interlock each other, making the sliding and friction of IRS particles more complex than that of particles with regular shape. In order to study the interaction of IRS particles more efficiently, a refined method of constructing discrete element model based on computed tomography scanning of IRS particles is proposed. Three parameters were introduced to control the accuracy and the number of packing spheres. Subsequently, the inertia tensor of the IRS particle model was optimized. Finally, laboratory and numerical open bottom cylinder tests were carried out to verify the refined modeling method. The influence of particle shape, particle position, and mesoscopic friction coefficient on the interaction of particles was also simulated. It is noteworthy that with the increase of mesoscopic friction coefficient, the fluidity of IRS particle assembly decreases, and intermittent limit equilibrium state may appear. Copyright © 2014 John Wiley & Sons, Ltd.     

定量表征河流砂颗粒的形状——在西藏帕隆藏布的尝试

河流搬运体系中,颗粒的形状属性可以反映其来源、搬运、沉积的过程。长期以来沉积学界缺乏相对统一的定量的、多维的颗粒形状参数。本研究以西藏雅鲁藏布江支流帕隆藏布为例,运用图像分析、软件分析等计算机辅助技术,对8个天然河流砂样品中的2276个颗粒开展碎屑成分鉴定和形状参数量化等工作。重点评估了11个颗粒形状参数,通过因子分析等统计方法提取并定义了3个形状信号,包括圆形度因子、规则度因子和平滑度因子。这些参数可用于表征颗粒宏观形状上接近圆形的程度、中观尺度上轮廓规则的程度及轮廓平滑的程度。挖掘天然河流砂的重要形状特征后,进一步探索了这些形状参数与颗粒成分、粒度的关系。本研究成果为解释河流系统中颗粒的形状信号、理解颗粒自身微观属性和外部物理过程等提供重要借鉴。     

定量表征河流砂颗粒的形状——在西藏帕隆藏布的尝试

河流搬运体系中,颗粒的形状属性可以反映其来源、搬运、沉积的过程。长期以来沉积学界缺乏相对统一的定量的、多维的颗粒形状参数。本研究以西藏雅鲁藏布江支流帕隆藏布为例,运用图像分析、软件分析等计算机辅助技术,对8个天然河流砂样品中的2276个颗粒开展碎屑成分鉴定和形状参数量化等工作。重点评估了11个颗粒形状参数,通过因子分析等统计方法提取并定义了3个形状信号,包括圆形度因子、规则度因子和平滑度因子。这些参数可用于表征颗粒宏观形状上接近圆形的程度、中观尺度上轮廓规则的程度及轮廓平滑的程度。挖掘天然河流砂的重要形状特征后,进一步探索了这些形状参数与颗粒成分、粒度的关系。本研究成果为解释河流系统中颗粒的形状信号、理解颗粒自身微观属性和外部物理过程等提供重要借鉴。     

动态图像法应用于海滩沉积物粒度粒形测试及其与筛析法的比较

筛析法是海滩沉积物粒度分析较经典和常用的方法。随着科学技术的发展,利用动态图像法分析沉积物粒度逐渐得到推广。本文利用动态图形法和筛析法对海南岛5个海滩剖面20组沉积物样品进行粒度粒形测试,并将两种方法所测得的粒度进行比较。测试分析结果显示,该方法的测试结果重复性好,精度高;通过与筛析法的对比显示,动态图像法与筛析法的测试结果非常接近,粒度级配曲线基本一致,各个粒度参数值很接近且相关性非常好（R2>0.94）;由动态图像法得出的粒形参数可以看出,粒径相当的不同海滩沉积物粒形参数有很大差别,同一海滩不同部位的球形度和宽长比变化很大,对称度和凹凸度变化稍小。研究表明,动态图像法与筛析法之间的粒度分析差异主要来自于两者测量原理的不同和天然海滩砂颗粒形状的不规则;动态图像法解决了不规则沉积物粒度的测量。因此,动态图像法可以替代筛析法来测量沉积物粒度,应用前景广阔。     

基于测井参数优选的煤层含气量预测模型

随着煤层气勘探的不断深入,对煤层含气量预测精度提出了更高的要求。基于煤层含气量测井响应特征,分析测井参数与含气量的相关性,提出MIV（Mean Impact Value）技术与LSSVM（Least Squares Support Vector Machine）结合的测井参数优选策略,优选最优测井参数作为网络建模的输入自变量组合,通过粒子群算法优化LSSVM网络核心参数,最后构建一套适用于煤层含气量预测的MIV-PSO-LSSVM模型。在此基础上,分别对比分析LSSVM、PSO-LSSVM、MIV-LSSVM和MIV-PSO-LSSVM模型对煤层含气量的预测性能,并与传统多元回归方法进行了对比,利用拟合优度和均方根误差对此5类模型进行评价。结果表明:PSO优化下的LSSVM模型预测精度得到有效提升,结合MIV方法优选测井参数可大幅度改善神经网络建模性能,MIV-PSO-LSSVM模型可实现煤层含气量高精度预测,为煤层气勘探及其储层评价提供新的技术支撑,且本研究的建模策略及思想可广泛应用于其他机器学习建模研究领域。      

粗粒料强度及变形特性的细观模拟

粗粒料是一定级配的岩石颗粒集合体,具有独特的物理力学特性。以粗粒料室内三轴固结排水试验成果为基础,基于离散元颗粒流理论,从细观角度出发,以PFC3D为工具,通过自编程及二次开发,得到按级配生成的粗粒料三轴试验数值模型。引入clump颗粒考虑颗粒形状对粗粒料强度及变形的影响,分析剪胀、颗粒形状、颗粒重排的关系。结果表明：颗粒形状是影响粗粒料强度与变形的主要因素,在其他细观参数一定的情况下,改变颗粒形状,可以显著影响粗粒料的力学行为;BPM模型的应力-应变关系只在低围压下与试验值吻合,随着围压的增大,偏差越来越大;而引入clump颗粒的PFC3D数值模型能很好地模拟粗粒料室内三轴固结排水试验的应力-应变特性,但由于BPM及clump都是刚性颗粒,没有考虑颗粒变形及破碎,造成应变剪胀偏大。     

基于支持向量机的膨胀土胀缩等级预测

膨胀土的胀缩等级判定对膨胀土地区工程建设具有重要的意义。为此,本文提出了一种基于支持向量回归机（SVR）模型的膨胀土胀缩等级预测方法。基于肯尼亚“蒙内铁路”沿线膨胀土的土工试验数据,以土体自由膨胀率作为预测目标,构建了包含两种不同预测指标体系的膨胀土胀缩等级预测模型。模型I以液限、塑限、塑性指数、3种不同粒径的颗粒含量（< 0.075、0.075～0.25、0.25～0.5）、土的类型为输入参数,模型II以液限、塑限、塑性指数、粒径< 0.075的颗粒含量、土的类型为预测参数。两个模型在预测时采用Linear、Polynomial、RBF和Sigmoid核函数进行训练。结果表明:（1）当预测采样次数达到1000次时,训练模型均趋于稳定;（2）整体而言,模型I的预测精度要优于模型II,模型I中采用RBF核函数建立的模型给出了最高准确率86.6%,其次为Linear核函数（准确率82.9%）和Sigmoid和函数（准确率75.1%）。模型II中采用RBF核函数建立的模型给出了最高准确率77.4%,其次为Linear核函数（准确率74.3%）和Sigmoid和函数（准确率72.9%）;（3）采用Linear函数、Sigmoid函数和RBF函数作为核函数模型对44组未知胀缩等级的土样预测时,模型I中三者预测结果相同的数量占比为73%,其余组土样的预测胀缩等级相同或相邻,不存在“越级”现象,模型II中三者预测结果相同的数量占比为68%,不存在“越级”现象。最后,通过与模糊层次分析法评价结果对比,进一步证明了本文研究结果可为肯尼亚等类似地区工程建设中膨胀土的胀缩等级预测和处理提供依据。     

Particle shape: a review and new methods of characterization and classification

Shape is a fundamental property of all objects, including sedimentary particles, but it remains one of the most difficult to characterize and quantify for all but the simplest of shapes. Despite a large literature on the subject, there remains widespread confusion regarding the meaning and relative value of different measures of particle shape. This paper re‐examines the basic concepts of particle shape and suggests a number of new and modified methods which are widely applicable to a range of sedimentological problems; it is shown that the most important aspects of particle form are represented by the I/L ratio (elongation ratio) and S/I ratio (flatness ratio). A combination of these two ratios can be used to classify particles in terms of 25 form classes. A method of obtaining a quantitative measure of particle roundness using simple image analysis software is described, and a new visual roundness comparator is presented. It is recommended that measurements of both roundness and circularity (a proxy measure of sphericity) are made on grain images in three orthogonal orientations and average values calculated for each particle. A further shape property, irregularity, is defined and a classification scheme proposed for use in describing and comparing irregular or branching sedimentary particles such as chert and coral.     

Particle shape effect on thermal conductivity and shear wave velocity in sands

This study presents the correlations between quantified shape parameters and geotechnical properties for nine sand specimens. Four shape parameters, sphericity, convexity, elongation and slenderness, were quantified with two-dimensional microscopic images with the aid of image processing techniques. An instrumented oedometer cell is used to measure compressibility, thermal conductivity and shear wave velocity during loading, unloading and reloading stages. As the particle shape inherently determines the initial loose packing condition, initial void ratio and shape parameters are well correlated with compressibility. The applied stress in soils increases the interparticle contact area and contact quality; round particles tend to achieve higher thermal conductivity and shear wave velocity during stress-induced volume change. Multiple linear regression is implemented to capture the relative contributions of involved variables, revealing that the thermal evolution is governed by the initial packing density and particle shape. The experimental observations underscore the predominant effect that particle shape has on the geomechanical and physical properties upon stress-induced soil behavior.     

Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.     

基于微观结构重塑的非饱和冻土导热系数预测

为预测非饱和冻土的导热性能,基于土体微观结构,提出了非饱和冻土特征结构识别算法和多元素生成算法,并将该算法与传统有限单元法组合,建立非饱和冻土导热系数蒙特卡洛预测模型。通过土体SEM电镜图像,采用逆向四参数增长识别法识别土体中各组分含量、大小以及各方向分布概率;改进传统的四参数随机增长法,提出了考虑土、水、冰和气的多元素生成算法;基于生成的非饱和冻土模型,通过蒙特卡洛方法获得非饱和冻土导热系数,并与规范中冻土导热系数进行对比,验证了蒙特卡洛法预测模型的合理性（平均误差<4%）;通过多因素分析研究孔隙率、颗粒大小、土体导热性、饱和度以及结冰率对非饱和冻土导热性影响,各因素与导热系数的相关系数依次为：-0.352、-0.098、0.641、0.520和0.060,影响大小为：土颗粒导热性>饱和度>孔隙率>土颗粒大小>结冰率。各影响因素对非饱和冻土导热系数影响可以归纳为对热通量形成“热链”密度、宽度、连通性、热流承载力以及对“热桥”通量的影响。     

基于深度学习的山东大尹格庄金矿床深部三维预测模型

在隐伏矿体三维预测中,预测模型的准确性在很大程度上取决于找矿指标对矿化富集部位的指示性。然而,找矿指标容易受到找矿概念模型可靠性和成矿信息提取有效性限制,从而影响预测的准确性。论文以山东大尹格庄金矿隐伏矿体三维预测为例,基于深度学习方法,构建矿床深部隐伏矿体三维预测模型,旨在利用深度网络模型,学习获得对矿化具有显著指示性的找矿指标,提升三维预测的准确性。该方法将三维地质模型及其形态特征转换为适合卷积网络二维图像,采用卷积神经网络实现找矿指标的自动提取,并构建三维地质模型到矿化富集地段的定量关联。利用该方法建立了大尹格庄金矿的三维预测模型,经与几种人工建立找矿指标预测模型的对比分析,表明基于深度学习的预测模型较大地提升了预测准确性。     

基于融合算法的土石混合体斜坡稳定性预测

土石混合体是物理力学性质较为复杂的地质材料,因此该类斜坡的稳定性评价是工程地质领域的重要课题。为提高斜坡稳定性预测的能力,本文将粒子群算法和果蝇优化算法相互耦合,形成融合算法,并结合机器学习模型,使用决定系数、均方误差和平均绝对误差3个评价指标,构建并评价土石混合体斜坡稳定性的预测模型,最终采用基于融合算法的梯度提升决策树模型对输入参数进行了重要性分析。结果表明：(1)相比于粒子群和果蝇优化算法,融合算法能够有效优化机器学习模型的参数,从而较为明显地提升模型预测精度。(2)基于融合算法的梯度提升决策树模型预测精度最高,达到93.33%,明显优于融合算法下的决策树模型和Stacking模型。(3)影响土石混合体斜坡稳定性的结构因素,其重要性从高到低分别为基覆面倾角、含石率、总体坡角、坡高。     

砂土颗粒形状量化及其对力学指标的影响分析

砂作为一种特殊的散体材料,其宏观物理力学性质,如密实度、剪切特性（临界状态角,剪胀角）、压缩性及颗粒破碎特征等均受到颗粒形状的影响,目前为止,对于砂粒土颗粒形状的量化工作,未到达成熟阶段。试验采用普通光学显微镜获取3种不同砂颗粒及一种相似材料（玻璃球）数字图像,利用ImageJ图形软件对其进行黑白二值化处理,获取颗粒形状轮廓边界;从3个层次定义颗粒形状参数,并利用java语言编制形状量化插件程序,计算砂粒各形状参数值,最后通过相对密度试验、直剪试验测试不同砂样的极限孔隙比、剪切强度指标。试验结果表明：整体轮廓系数、球形度、棱角度3项形状参数可作为不同砂粒形状鉴别和量化的关键参数,且与剪胀角、临界状态摩擦角均具有良好的相关性,试验提供了一种量化砂颗粒形状的有效方法,并可将得到的关键量化参数应用到宏观力学性质分析与数值模拟工作中     

PSO-LIBSVM在钾盐矿层识别中的应用研究

钾盐的紧缺严重制约了中国农业的发展,加大钾盐的勘探开发力度有助于提高我国钾盐的自给自足能力。四川盆地钾盐资源丰富,是我国目前重要的钾盐勘探开发研究区域之一。杂卤石作为四川盆地最重要的固态钾盐矿物,常夹杂在硬石膏、岩盐和白云岩等岩层中。针对常规测井解释方法难以精确识别杂卤石的问题,因此,提出一种新的基于粒子群算法(PSO)优化的支持向量机(SVM)杂卤石识别方法开展四川盆地杂卤石的分类识别研究。以PSO和SVM理论为基础,结合测井解释方法,选择对杂卤石测井响应灵敏的有效数据作为输入样本,随机产生训练集和测试集,并采用PSO优选出径向基核函数参数,建立杂卤石分类预测模型。与录井结果对比,基于PSO的SVM模型识别准确率达到了97.5758%,在识别精度和速度上明显优于交叉验证方法优化的SVM模型。结果表明,该模型在四川盆地钾盐勘探中具有广阔的应用前景。     

Simulation of mixed-mode fracture using SPH particles with an embedded fracture process zone

This paper focuses on the modelling of mixed-mode fracture using the conventional smoothed particle hydrodynamics (SPH) method and a mixed-mode cohesive fracture law embedded in the particles. The combination of conventional SPH and a mixed-mode cohesive model allows capturing fracture and separation under various loading conditions efficiently. The key advantage of this framework is its capability to represent complex fracture geometries by a set of cracked SPH particles, each of which can possess its own mixed-mode cohesive fracture with arbitrary orientations. Therefore, this can naturally capture complex fracture patterns without any predefined fracture topologies. Because a characteristic length scale related to the size of the fracture process zone is incorporated in the constitutive formulation, the proposed approach is independent from the spatial discretisation of the computational domain (or mesh independent). Furthermore, the anisotropic fracture responses of materials can be naturally captured thanks to the orientation of the fracture process zone embedded at the particle level. The performance of the proposed approach demonstrates its potentials in modelling mixed-mode fracture of rocks and similar quasi-brittle materials.