Fractal Reconstruction with Unorganized Geochemical Data

Fractal geometry is receiving increased attention as a model for many natural phenomena. In this paper, we address two issues in dealing with unorganized 3-D measured data using fractal geometry: extraction of fractal characters and fractal surface (geochemical landscapes) reconstruction. We start from a set of randomly measured 3-D data on a plane. After classification and concentration of the input data, we present methods for estimating the fractal dimension and texture deviation as fractal characters, and then an improved subdivision scheme is developed to reconstruct fractal surface on the basis of the extracted fractal characters. We demonstrate both characterization and reconstruction with irregularly measured geochemical data from 1767 stream sediment samples in the middle district (450km² in area) of Zhejiang, China.     

On the practice of estimating fractal dimension

Coastlines epitomize deterministic fractals and fractal (Hausdorff-Besicovitch) dimensions; a divider [compass] method can be used to calculate fractal dimensions for these features. Noise models are used to develop another notion of fractals, a stochastic one. Spectral and variogram methods are used to estimate fractal dimensions for stochastic fractals. When estimating fractal dimension, the objective of the analysis must be consistent with the method chosen for fractal dimension calculation. Spectal and variogram methods yield fractal dimensions which indicate the similarity of the feature under study to noise (e.g., Brownian noise). A divider measurement method yields a fractal dimension which is a measure of complexity of shape.     

Uncertainty in fractal dimension estimated from power spectra and variograms

The reliability of using fractal dimension (D) as a quantitative parameter to describe geological variables is dependent mainly on the accuracy of estimated D values from observed data. Two widely used methods for the estimation of fractal dimensions are based on fitting a fractal model to experimental variograms or power-spectra on a log-log plot. The purpose of this paper is to study the uncertainty in the fractal dimension estimated by these two methods. The results indicate that both spectrum and variogram methods result in biased estimates of the D value. Fractal dimension calculated by these two methods for the same data will be different unless the bias is properly corrected. The spectral method results in overestimated D values. The variogram method has a critical fractal dimension, below which overestimation occurs and above which underestimation occurs. On the bases of 36,000 simulated realizations we propose empirical formulae to correct for biases in the spectral and variogram estimated fractal dimension. Pitfalls in estimating fractal dimension from data contaminated by white noise or data having several fractal components have been identified and illustrated by simulated examples.     

Fractal dimensions and geometries of caves

Lengths of all caves in a region have been observed previously to be distributed hyperbolically, like self-similar geomorphic phenomena identified by Mandelbrot as exhibiting fractal geometry. Proper cave lengths exhibit a fractal dimension of about 1.4. These concepts are extended to other self-similar geometric properties of caves with the following consequences.Lengths of a cave is defined as the sum of sizes of passage-filling, linked modular elements larger than the cave-defining modulus. If total length of all caves in a region is a self-similar fractal, it has a fractal dimension between 2 and 3; and the total number of linked modular elements in a region is a self-similar fractal of the same dimension. Cave volume in any modular element size range may be calculated from the distribution.The expected conditional distribution of modular element sizes in a cave, given length and modulus, also is distributed hyperbolically. Data from Little Brush Creek Cave (Utah) agree and yield a fractal dimension of about 2.8 (like the Menger Sponge). The expected number of modular elements in a cave equals approximately the 0.9 power of length of the cave divided by modulus. This result yields an intriguing parlor trick. An algorithm for estimating modular element sizes from survey data provides a means for further analysis of cave surveys.     

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.     

Application of the content–area (C–A) fractal model and prediction–area (P–A) plot for mineral prospectivity modeling in the Luchun area of Yunnan Province,China

This method of assigning weights based on expert opinion introduces bias when we are evaluating the relative importance of evidence values. In this paper, we used a prediction–area (P–A) plot method and content–area (C–A) fractal model to estimate the weight of each evidence map. In this paper, we used the content region (C–A) fractal model to divide the evidence maps to the threshold of the corresponding dimensions. The P–A plot approach is an objective data-driven approach for evaluating map weights. Using geochemical layer and remote sensing, hydroxyl layers as weight evidence maps are the highlights of this study. We use the P–A method from which we can evaluate the predictive ability of each evidence map with respect to the known ore occurrences. We used the P–A plot for weighting each evidence map and choosing the appropriate threshold for predictor maps in the Luchun area of Yunnan Province, China. The method adopted in this paper can improve the prediction efficiency of ore prospecting.     

利用毛管压力曲线分形分维方法研究流动单元

利用取心井铸体薄片获得的图像资料和毛管压力曲线,通过图像分形几何学方法以分维数的形式定量地表征出了复杂的微观孔隙喉道结构特征,发现能够很好地划分和评价孔隙岩石中油、气、水的渗流差异,可以用于储层微观流动单元表征。文中阐述了岩石微观孔隙喉道结构分形的理论基础、计算方法和应用于表征流动单元的依据。建立了中国西部砾岩低渗透油藏微观孔隙喉道分维数与孔隙度、渗透率之间计算图版,据此在油藏中利用常规测井资料获得的孔隙度、渗透率参数计算微观孔隙喉道分维数,开展全油藏流动单元划分与评价,取得了良好的效果。研究结果表明,利用毛管压力曲线分形分维方法研究储层微观流动单元是一种很有效的途径。     

地质现象分形统计学研究的若干问题

摘 要 要发展分形理论在地质科学中的应用‚就必须加强对地质现象分形统计学的研究。 文中对地质现象分形统计学的概念、意义、内容、方法和一般步骤作一概略论述。同时‚对 分形概念的拓广、分维求法及意义、幂律问题、分数布朗运动、方位 分维估值法等问题也作 了初步的探讨。     

土粒度分维的确定方法探讨

本文通过对目前国内外研究土的粒度分维的三种方法的理论进行分析并一实际土样进行了比较,得到利用质量法求得土的粒度是三维方法中最方便实用的方法。     

Estimating fractal dimension of profiles: A comparison of methods

This paper examines the characteristics of four different methods of estimating the fractal dimension of profiles. The semi-variogram, roughness-length, and two spectral methods are compared using synthetic 1024-point profiles generated by three methods, and using two profiles derived from a gridded DEM and two profiles from a laser-scanned soil surface. The analysis concentrates on the Hurst exponent H,which is linearly related to fractal dimension D,and considers both the accuracy and the variability of the estimates of H.The estimation methods are found to be quite consistent for Hnear 0.5, but the semivariogram method appears to be biased for Happroaching 0 and 1, and the roughness-length method for Happroaching 0. The roughness-length or the maximum entropy spectral methods are recommended as the most suitable methods for estimating the fractal dimension of topographic profiles. The fractal model fitted the soil surface data at fine scales but not at broad scales, and did not appear to fit the DEM profiles well at any scale.