贵阳鱼简河水库坝址区岩体结构面的多重分形

研究了贵阳市鱼简河水库坝址区岩体结构面的多重分形。在研究区分别运用不同剖面岩体结构面测量统计结果进行多重分形分析,首先,利用选定的A区进行结构面多重分形分析,依次选取不同的单元尺度值将该区域划分为正方形网格并测量出每个单元的信息量;取适当不同的实数值,计算分配系数;利用直线拟合方法算出幂指数;利用有关方程,求出多重分维值、奇异指数和分维谱函数。然后,运用同样方法对其它选定的区域进行多重分形,分别计算出多重分维值、奇异指数和分维谱函数并进行对比,得出A区岩体结构面的多重分维值、奇异指数和分维谱函数最大值和平均值均较大,表明A区岩体结构面较发育且复杂,亦说明A区岩体的工程性质较差,并根据多重分形结果列出了各岩体质量优劣的大致排列顺序。     

流域地貌形态特征多重分形算法研究

为研究流域地貌形态特征的量化指标,提出基于高程分布概率的多重分形计算模型,结合大理河岔巴沟流域和大堡岔流域DEM数据进行多重分形计算,对流域地貌形态特征多重分形谱的表征意义进行了探讨。结果表明:流域地貌形态特征多重分形谱可以更加敏感、更加全面地对流域地貌形态的总体特征进行描述;多重分形谱的顶点对应简单分形的容量维,多重分形谱的宽度可以定量表征流域表面的起伏程度,多重分形谱的端点维数的差别可以间接地反映流域峰谷数目的比例;流域地貌形态特征多重分形谱能够分层次地刻画流域内部的精细结构,从而更加突出地表现异常局部地貌变化特征;流域地貌形态特征多重分形谱的无标度区间的合理范围,应该确定为DEM像元尺度到流域最大高程值的1.5倍左右最为合适。     

航磁数据多重分形研究及应用

利用航磁异常场的自仿射分形特征,计算出其分维值,把分维值转化成图形,进而分析其所反映的深层次构造特征。最后对分维值进行了拟合分析     

四川地区断层空间分布的多重分形特征

断层系统多重分形避免了容量维Ｄ０的不足，考虑了每一条断层对整体分维的贡献，反映了断层的空间分布特征。根据四川地区断层分布图，运用多重分形理论检验了断层空间分布的自相似性，分区计算了四川地区各带的多重分维值。结果表明，断层破裂系统为分形结构，具有很好的自相似性，相关系数均达０．９９。不同带分维值的较大差异，反映不同构造区域的断层分布特点。最后，对分维的含义进行了深入探讨，对油气勘探的有利区域进行了分析。     

丹江口水库库区及周边地区河网形态、地貌特征及构造活动性意义

河流作为构造-气候相互作用最为敏感的地貌单元,记录了丰富的水系演化、构造变形以及气候变化等信息。通过研究河流的形成与演化过程来阐述区域地貌和构造活动特征是构造地貌研究的一个突破点,河流形态的空间变化是阐述河流形成与演化特征最为直观有效的方法。丹江口水库库区地貌特征复杂、地质灾害频发、差异性构造活动较为强烈,是开展构造地貌研究的理想场所。通过对丹江口水库库区及周边地区河流形态特征、地貌特征及构造活动特征等进行综合分析发现,河网分维值空间特征与区域内构造活动性较强的断裂的空间分布高度吻合,构造活动性较强的断裂带及周边地区,河网受到构造活动的影响,发育不成熟,河网分维值出现低值,分维值均小于1.115;构造活动性较弱的断裂带及周边地区,河网发育过程中未受到构造活动的影响,发育较成熟,河网分维值高,分维值均大于1.25;而河网分维值空间变化与地形坡度、平均高程等地貌参数相关性不显著。因此,区域构造活动性特征是河网形态空间变化控制的关键因素。利用河网形态的分维特征量化区域构造活动的强弱及各区域构造活动的差异,对于河流的形成与演化、构造活动性及预测地质灾害的发生等方面的研究都有一定的参考价值。      

新疆东准噶尔地区断裂的多重分形研究

结合东准噶尔地区强应变构造带的空间展布情况和断裂的空间分布特征 ,用多重分形模型测算了各带的多重分维值。研究结果表明 :强应变构造带在不同尺度上具有自相似性 ;断裂的空间分布为多重分形结构 ,断裂的演化有从复杂几何结构的次级断裂组合向单一连续的大断裂过渡而分维值逐渐减小的趋势 ;不同的大地构造单元有不同的分维值 ,而且有靠近板块缝合带 (或海沟 )分维值减小、远离板块缝合带 (或海沟 )分维值增大的趋势     

任楼矿井72煤层断层构造分形特征研究

利用分形理论,对安徽皖北任搂煤矿72煤层断层分形特征进行了研究,计算了不同构造分区的维数。研究发现构造复杂性不同及断裂性质的不同其分形维数也有所不同,构造简单,断层分维数小,而构造复杂区,断层分为数相对较大;剪切断裂带具有较低的分维值,张扭性断裂带分维值较高。     

地表的分形测量及其大地构造学意义

以湖北红安地区为例,采用投影覆盖法(projectivecoveringmethod)对地表进行了二维分形测量,结果表明,地表面积具有双分形(bifractal)关系,即具有小尺度的结构分形(texturalfractal)和大尺度的构造分形(structuralfractal),分叉点(breakpoint)的尺度为3610m,分维值都在2～3之间且结构分维值大于其构造分维值。可见,地表形态具有分形性质,分维值可以指示地表形态的复杂程度。构造分维值可作为构造活动强度的一个指标,可为大地构造单元的划分提供定量依据。复杂地表形态主要是由构造活动(内营力作用)和各种复杂表生地质作用(外营力作用)引起的,前者主要控制大尺度的地形起伏,后者则塑造小尺度的地表形态。地表分维值可以指示地表的发育成熟度,该地区小尺度的结构分维值大于大尺度的构造分维值表明其处于地表形态的发育晚期。此外,地表的分形尺度可以来用确定构造活动尺度,从而指导构造地质与找矿勘探研究。     

岩溶形态系统的分形特征及其机理探讨

运用分形理论，系统研究了构成岩溶形态系统的岩溶地貌、水文网和洞穴系统的分形特征，并计算了分维值，以鄂西岩溶区的地貌小区为例，建立了地表峰体的分维与控制岩溶系统发育的岩性，外源水等因素的联系，揭示了分维的岩溶学含义，进而从岩溶系统介质场结构及岩溶作用过程的非线性动力特征分析探讨了岩溶形态系统的分形机理。     

西太平洋海域海山地形分形特征研究

对太平洋麦哲伦海山区、威克—马尔库斯海山区、马绍尔群岛、中太平洋海山区及莱恩群岛5个海山区的平顶海山与尖顶海山地形进行分形研究,结果表明两种类型海山地形具有不同的分形特征。海山形态投影覆盖法揭示平顶海山具有单分形的大尺度构造分形,尖顶海山具有大尺度构造分形和小尺度结构分形的双分形结构。海山等高线尺度法分形结果显示,同一海山区平顶海山分维值小于尖顶海山的;同一海山不同高程段等高线分维值基本保持稳定,垂向上具有明显的分段性,可以参考并使用它进行地貌垂向分带。     

Some Distinctions Between Self-Similar and Self-Affine Estimates of Fractal Dimension with Case History

Compass, power-spectral, and roughness-length estimates of fractal dimension are widely used to evaluate the fractal characteristics of geological and geophysical variables. These techniques reveal self-similar or self-affine fractal characteristics and are uniquely suited for certain analysis. Compass measurements establish the self-similarity of profile and can be used to classify profiles based on variations of profile length with scale. Power spectral and roughness-length methods provide scale-invariant self-affine measures of relief variation and are useful in the classification of profiles based on relative variation of profile relief with scale. Profile magnification can be employed to reduce differences between the compass and power-spectral dimensions; however, the process of magnification invalidates estimates of profile length or shortening made from the results. The power-spectral estimate of fractal dimension is invariant to magnification, but is generally subject to significant error from edge effects and nonstationarity. The roughness-length estimate is also invariant to magnification and in addition is less sensitive to edge effects and nonstationarity. Analysis of structural cross sections using these methods highlight differences between self-similar and self-affine evaluations. Shortening estimates can be made from the compass walk analysis that includes shortening contributions from predicted small-scale structure. Roughness-length analysis reveals systematic structural changes that, however, cannot be easily related to strain. Power-spectral analysis failed to extract useful structural information from the sections.     

成矿元素品位有序数据集自仿射分形方法应用性评价

自仿射分形的Hurst指数是分析地质剖面数据的有利参数。以大尹格庄金矿不同勘探线刻槽取样所得的金品位序列为例，评价Hurst指数的几种估算方法在地质剖面数据分析中的适用性。取相同尺度，全部数据集的增量标准偏差法统计散点呈波状变化，部分数据集的曲线长度变换法统计散点的后半部分呈波状变化,全部数据集的重标极差分析法统计散点线性拟合较好。结果显示增量标准偏差法对尺度要求较为苛刻，适于巨量数据的统计；曲线长度变换法应用性较广，所得Hurst指数反映品位的空间变化强度；重标极差分析法稳定性最好，其Hurst指数反映了品位变化相依性。      

基于图像分割的煤岩割理CT图像各向异性特征

利用工业CT对自然煤岩样进行断层扫描观测。针对煤岩裂隙系统的多尺度、各向异性特征,应用Canny算子图像分割与方向性边缘检测技术,提取煤岩CT图像割理的总体特征、水平方向和垂直方向特征;根据特征图像计算了煤岩样的总体分形维数、孔隙度,各向异性分形维数与孔隙度及其在三维空间中的分布;讨论了分形维数与孔隙度、渗透率之间的关系,并根据煤岩样的分形维数、孔隙度对实际工程岩体的孔隙度和渗透率进行了外推计算。研究表明,煤岩样不同扫描断面的分形维数和孔隙度不同,同一煤样同一断面不同方向的分形维数与孔隙度亦不相同。利用图像分割与边缘检测对工业CT图像进行分析,可以对煤岩的各向异性分形维数与孔隙度在2D与3D空间进行精细描述。      

Requirements for accurate estimation of fractal parameters for self-affine roughness profiles using the line scaling method

Summary A new concept of feature size range of a roughness profile is introduced in the paper. It is shown that this feature size range plays an important role in estimating the fractal dimension,D, accurately using the divider method. Discussions are given to indicate the difficulty of using both the divider and the box methods in estimatingD accurately for self-affine profiles. The line scaling method's capability in quantifying roughness of natural rock joint profiles, which may be self-affine, is explored. Fractional Brownian profiles (self-affine profiles) with and without global trends were generated using known values ofD, input standard deviation, , and global trend angles. For different values of the input parameter of the line scaling method (step sizea ₀),D and another associated fractal parameterC were calculated for the aforementioned profiles. Suitable ranges fora ₀ were estimated to obtain computedD within ±10% of theD used for the generation. Minimum and maximum feature sizes of the profiles were defined and calculated. The feature size range was found to increase with increasingD and , in addition to being dependent on the total horizontal length of the profile and the total number of data points in the profile. The suitable range fora ₀ was found to depend on bothD and , and then, in turn, on the feature size range, indicating the importance of calculating feature size range for roughness profiles to obtain accurate estimates for the fractal parameters. Procedures are given to estimate the suitablea ₀ range for a given natural rock joint profile to use with the line scaling method in estimating fractal parameters within ±10% error. Results indicate the importance of removal of global trends of roughness profiles to obtain accurate estimates for the fractal parameters. The parametersC andD are recommended to use with the line scaling method in quantifying stationary roughness. In addition, one or more parameters should be used to quantify the non-stationary part of roughness, if it exists. The estimatedC was found to depend on bothD and and seems to have potential to capture the scale effect of roughness profiles.     

Natural fracture profiles,fractal dimension and joint roughness coefficients

Summary Thirteen natural rock profiles (Barton and Choubey, 1977) are analyzed for their fractal properties. Most of the profiles were found to approximate fractal curves but some also showed features of specific wavelengths and amplitudes superimposed on fractal characteristics. The profiles showed fractal dimensions from 1.1 to 1.5 covering a range of selfsimilar and self-affine curves. The analysis results suggest a negative correlation between fractal dimension,D, and amplitude,A. Joint roughness coefficients (JRC) show a positive correlation with amplitude,A, and a negative correlation with fractal dimension,D. A numerical model of fracture closure is used to investigate the effects of different profile characteristics (D, A and sample size) on the nature of dilation and contact area, using the natural profiles and synthetic fractional Brownian motion profiles. Smooth profiles (low JRC, highD, lowA) display many small contact regions whereas rough fractures (high JRC, lowD, highA) display few large contact areas. The agreement with published experimental data supports the suggested correlations between JRC and the fractal parameters,A andD. It is suggested that observed scale effects in JRC and joint dilation can be explained by small differential strain discontinuities across fractures, which originate at the time of fracture formation.     

Determination of Joint Roughness Coefficients Using Roughness Parameters

This study used precisely digitized standard roughness profiles to determine roughness parameters such as statistical and 2D discontinuity roughness, and fractal dimensions. Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval.     

Fractal models for predicting soil hydraulic properties: a review

Modern hydrological models require information on hydraulic conductivity and soil-water retention characteristics. The high cost and large spatial variability of measurements makes the prediction of these properties a viable alternative. Fractal models describe hierarchical systems and are suitable to model soil structure and soil hydraulic properties. Deterministic fractals are often used to model porous media in which scaling of mass, pore space, pore surface and the size-distribution of fragments are all characterized by a single fractal dimension. Experimental evidence shows fractal scaling of these properties between upper and lower limits of scale, but typically there is no coincidence in the values of the fractal dimensions characterizing different properties. This poses a problem in the evaluation of the contrasting approaches used to model soil-water retention and hydraulic conductivity. Fractal models of the soil-water retention curve that use a single fractal dimension often deviate from measurements at saturation and at dryness. More accurate models should consider scaling domains each characterized by a fractal dimension with different morphological interpretations. Models of unsaturated hydraulic conductivity incorporate fractal dimensions characterizing scaling of different properties including parameters representing connectivity. Further research is needed to clarify the morphological properties influencing the different scaling domains in the soil-water retention curve and unsaturated hydraulic conductivity. Methods to functionally characterize a porous medium using fractal approaches are likely to improve the predictability of soil hydraulic properties.     

甘肃黄土的粒度分维特征及意义

依据分形理论分析了甘肃黄土粒度分布的分形结构特征。黄土具有良好的统计自相似性。随着空间位置和黄土形成时代的不同,黄土的分形结构特征有明显的变化特征,其分维值介于2.29至2.56之间;且粒度分维值的大小与分选好坏密切相关,即:分选系数大则分维值大,反之则分维值小。这表明黄土的粒度分维在一定程度上可以反映黄土的形成演化特征。在此基础上进一步研究了黄土粒度分维与干密度、孔隙比、湿陷系数之间的定量关系,指出黄土分维值可以定量表征黄土的物理、水理性质。与地形地貌、水文和气候等因素一样,它也可作为自然区划的一个主要指标。