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摘要翻译：
本文对测量和表征粗糙轮廓的方法进行了测试，重点是测量自仿射粗糙度指数，并描述了一个简单的测试，以分离源于轮廓符号符号中长程相关性的粗糙度指数和源于跳跃的L{e}vy分布的粗糙度指数。通过对已知粗糙度指数的轮廓进行测试，发现功率谱密度分析和平均小波系数法对粗糙度指数在0.1～0.9范围内的估计效果最好。当剖面长度大于256时，误差棒小于0.03，估计中没有系统偏差。我们给出了误差条和系统误差的定量估计，以及它们与粗糙度指数和轮廓长度的关系。对于不同于功率谱密度分析和二阶相关函数法的测量方法，我们也量化了幂律噪声对测量粗糙度指数的修正。
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英文标题：
《The accuracy of roughness exponent measurement methods》
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作者：
Jan {\O}ystein Haavig Bakke, Alex Hansen
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最新提交年份：
2007
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分类信息：
一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics 物理学
二级分类：Materials Science 材料科学
分类描述：Techniques, synthesis, characterization, structure. Structural phase transitions, mechanical properties, phonons. Defects, adsorbates, interfaces
技术，合成，表征，结构。结构相变，力学性质，声子。缺陷，吸附质，界面
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英文摘要：
We test methods for measuring and characterizing rough profiles with emphasis on measurements of the self-affine roughness exponent, and describes a simple test to separate between roughness exponents originating from long range correlations in the sign signs of the profile, and roughness exponents originating from L{\'e}vy distributions of jumps. Based on tests on profiles with known roughness exponents we find that the power spectrum density analysis and the averaged wavelet coefficients method give the best estimates for roughness exponents in the range 0.1 to 0.9. The error-bars are found to be less than 0.03 for profile lengths larger than 256, and there are no systematic bias in the estimates. We present quantitative estimates of the error-bars and the systematic error and their dependence on the value of the roughness exponent and the profile length. We also quantify how power-law noise can modify the measured roughness exponent for measurement methods different from the power spectrum density analysis and the second order correlation function method.
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PDF链接：
https://arxiv.org/pdf/707.175
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