The frequency dependence of soil parameters has been confirmed by many experiments. Most of them are done by the measurement of soil sample in laboratories. Although some studies are based on field measurement, only homogeneous ground or two-layer model are considered. The study of frequency dependence of soil parameters with considering multilayered model is lacking. The frequency dependence of the horizontally multilayered soil model is studied by the inversion of soil parameters in the frequency domain. The inversion of frequency domain soil parameters is translated into an optimization problem. The model parameters are determined by spectral induced polarization data, and particle swarm optimization is applied to optimizing model parameters. The performances of three different methods to trap particles inside boundaries are compared. In order to improve the computational efficiency of the inversion program, parallel computing is combined with particle swarm optimization to solve the optimization problem. The execution time of the developed algorithm before and after the application of parallel computing is compared. The performances of other two optimization algorithms, simulated annealing and genetic algorithm, are compared with that of particle swarm optimization. So it would be more clear which optimization algorithm should be selected among these three commonly used algorithms when dealing with the inversion problem of soil parameters. The accuracy of the proposed method is verified by a numerical simulation experiment. Then, this method is applied to interpreting field data, and the frequency dependence of soil parameters can be observed from the inversion results.

中文翻译：

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考虑土壤参数频率相关性的用于推导水平多层土壤模型的粒子群优化全局优化算法

许多实验证实了土壤参数的频率依赖性。其中大部分是通过实验室对土壤样品的测量来完成的。虽然有些研究是基于现场测量的，但只考虑了均质地面或两层模型。缺乏考虑多层模型的土壤参数频率相关性的研究。通过在频域中反演土壤参数来研究水平多层土壤模型的频率依赖性。频域土壤参数的反演被转化为一个优化问题。模型参数由光谱激极化数据确定，应用粒子群优化算法优化模型参数。比较了在边界内捕获粒子的三种不同方法的性能。为了提高反演程序的计算效率，并行计算结合粒子群优化来求解优化问题。比较了所开发算法在应用并行计算之前和之后的执行时间。将模拟退火算法和遗传算法这两种优化算法与粒子群优化算法的性能进行了比较。所以在处理土体参数反演问题时，应该从这三种常用算法中选择哪一种优化算法就更清楚了。通过数值模拟实验验证了所提方法的准确性。然后，将该方法应用于野外数据的解释，从反演结果中可以观察到土壤参数的频率依赖性。