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The surface fractal dimension of the soil-pore interface as measured by image analysis

机译:通过图像分析测量的土壤-孔隙界面的表面分形维数

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There is general interest in quantifying soil structure in order to obtain physically based parameters relevant to transport processes. To measure the surface fractal dimension of the pore-solid interface we use approaches known from fractal geometry. The characteristics of this interface, expressed by its fractal dimension, are descriptors of the heterogeneity and complexity of soil structure. Samples of the Bt horizon of a Luvisol in loess were taken near Gottingen, Germany. To prepare thin sections, the material was dehydrated and embedded in resin. We obtained digital images at different magnifications from a field emission scanning electron microscope. Automatic image analysis was used to determine the corresponding surface fractal dimension by using the box counting and dilation methods, respectively. As the fractal dimension of a line (D-L) within a plain has been measured, the surface fractal dimension D-S is obtained by D-S = D-L + 1 assuming isotropy. We strongly focussed the calculation of the fractal dimension from the measured data files. The decision as to which data should be included between the lower and upper cutoffs is of fundamental significance to the final result. For the upper cutoff, we followed the convention that the scale range should not exceed 30% of the characteristic length (object or image size). Data derived from outside both cutoffs reflect structural proper-ties, either of pixels (lower cutoff) or of structuring elements (upper cutoff). Different methods were used to derive a mean surface fractal dimension for one magnification for (i) single images and (ii) each measurement step. Within the same range of scale, differences between the two methods (box counting and dilation) were smaller than the standard deviation of D-S. In contrast to our expectations for a mathematical fractal, we found decreasing values for D-S with increasing magnification. The values drift from D-S = 2.91 for a resolution of 2.44 mum/pixel to D-S = 2.58 for a resolution of 0.05 mum/pixel. By fitting two straight lines to the log-log plot, we found a crossover-point at a scale of about 14 mum, forming the border between textural and structural fractality. In addition, we will discuss further results obtained as well as possible sources of error.
机译:为了获得与运输过程有关的基于物理的参数,人们普遍对量化土壤结构感兴趣。为了测量孔-固界面的表面分形维数,我们使用了从分形几何学已知的方法。用分形维数表示的界面特征是土壤结构异质性和复杂性的描述。黄土中Luvisol的Bt视界样品是在德国哥廷根附近采集的。为了制备薄切片,将材料脱水并包埋在树脂中。我们从场发射扫描电子显微镜获得了不同放大倍数的数字图像。自动图像分析分别通过使用盒计数和膨胀方法来确定相应的表面分形维数。由于已经测量了平原内直线(D-L)的分形维数,假设各向同性,表面分形维数D-S通过D-S = D-L +1获得。我们非常关注从测量的数据文件计算分形维数。决定在上限和下限之间包含哪些数据对最终结果具有根本意义。对于上限,我们遵循以下约定:比例范围不应超过特征长度(对象或图像大小)的30%。从两个边界外部获取的数据反映了像素(下部边界)或结构元素(上部边界)的结构属性。对于(i)单张图像和(ii)每个测量步骤,使用不同的方法得出一个放大倍数的平均表面分形维数。在相同的刻度范围内,两种方法(盒计数和膨胀)之间的差异小于D-S的标准偏差。与我们对数学分形的期望相反,我们发现D-S的值随着放大率的增加而减小。该值从对于2.44 mum /像素的分辨率的D-S = 2.91到对于0.05 mum /像素的分辨率的D-S = 2.58漂移。通过将两条直线拟合到对数-对数图,我们发现了一个约14毫米比例的交叉点,形成了结构和结构分形之间的边界。此外,我们将讨论进一步获得的结果以及可能的错误来源。

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