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Eigendecomposition of TDR waveforms: a novel method to determine water content and pore fluid concentration of sandy soils

机译:TDR波形的本征分解:确定砂质土壤水分和孔隙液浓度的新方法

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摘要

In this study, an incident pulse signal of several harmonics (i.e., multiples of the fundamental frequency) was used as a source in the time domain reflectometry (TDR) probing technique. Reflected signals were captured by an oscilloscope and their characteristics were determined via eigendecomposition. Autoregressive modeling and singular value decomposition were used to calculate the eigenvalues and the most significant ones were identified based on power spectrum. A multivariate statistical analysis was performed for the two most dominant eigenvalues, which are dependent on water content and salt concentrations, and regression equations were obtained. To determine the water content and salt concentrations in terms of the first and second eigenvalues, a modified Powell hybrid algorithm was used to solve the obtained system of nonlinear equations. Actual and predicted results are in agreement indicating that the developed method is very successful in predicting water content and salt concentrations. Furthermore, on comparing the eigendecomposition method with Fourier spectral analysis, one can observe that the former is superior in predicting water content and salt concentrations.
机译:在这项研究中,几种谐波(即基频的倍数)的入射脉冲信号被用作时域反射法(TDR)探测技术中的信号源。反射信号由示波器捕获,并通过特征分解确定其特征。使用自回归建模和奇异值分解来计算特征值,并根据功率谱确定最重要的特征值。对取决于水含量和盐浓度的两个最主要特征值进行了多元统计分析,并获得了回归方程。为了根据第一和第二特征值确定水含量和盐浓度,使用改进的Powell混合算法来求解所获得的非线性方程组。实际结果和预测结果相符,表明所开发的方法在预测水含量和盐浓度方面非常成功。此外,通过将特征分解法与傅立叶光谱分析进行比较,可以观察到前者在预测水含量和盐浓度方面具有优势。

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