Analysis of the field dependence of remanent magnetization curves
Analysis of the field dependence of remanent magnetization curves
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DOI：
10.1029/2002jb002023
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发表时间：
2003-02-07
影响因子：
3.9
通讯作者：
Egli, R
中科院分区：
文献类型：
--
作者：
Egli, R
提出了一种计算和分析剩磁采集/退磁曲线矫顽力分布的新方法。通过重新缩放磁场和磁化轴，将采集/退磁曲线线性化。在评估矫顽力分布之前，对线性化曲线进行适当的滤波可以有效地消除测量误差。利用一组广义概率密度函数对过滤后的矫顽力分布进行建模，以估计不同磁分量的贡献。用解析法和数值法对这些函数进行误差估计，以评估模型是否与实测数据存在显著差异。利用该方法对瑞士Baldeggersee的3个沉积物样本和3个城市大气颗粒物（PM）样本进行了分析。研究发现，某些磁性组分的矫顽力分布与对数高斯函数有显著一致的偏差。单畴和多畴粒子的理论AF退磁曲线矫顽力分布也存在较大偏差。模型函数形状的约束会影响剩余磁化曲线中磁性分量的识别和定量，应尽量避免。本文提出的广义概率密度函数适用于高斯和大量非高斯矫顽力分布的适当建模。
[1] A new method to calculate and analyze coercivity distributions of measured acquisition/ demagnetization curves of remanent magnetization is presented. The acquisition/ demagnetization curves are linearized by rescaling both the field and the magnetization axes. An appropriate filtering of the linearized curves efficiently removes measurement errors prior to evaluating the coercivity distributions. The filtered coercivity distributions are modeled using a set of generalized probability density functions in order to estimate the contributions of different magnetic components. An error estimation is calculated for these functions with analytical and numerical methods in order to evaluate whether the model is significantly different from the measured data. Three sediment samples from Baldeggersee ( Switzerland) and three samples of urban atmospheric particulate matter ( PM) have been analyzed using this method. It is found that the coercivity distributions of some of the magnetic components show significant and consistent deviations from a logarithmic Gaussian function. Large deviations are found also in the coercivity distributions of theoretical AF demagnetization curves of single-domain and multidomain particles. Constraints in the shape of model functions affect the identification and quantification of magnetic components from remanent magnetization curves and should be avoided as far as possible. The generalized probability density function presented in this paper is suitable for appropriate modeling of Gaussian and a large number of non- Gaussian coercivity distributions.