Hydraulicproperties of soils could play an important role in affecting the partitioning of precipitation in the critical zone. In addition to traditional approaches, in the last two decades, many geophysical methods have been used to aid the hydrologic characterization and measurement of geological materials. In particular, the self-potential (SP) method shows great potential in these hydrogeophysical applications. The objective of this study is to evaluate whether the addition of SP data can improve the estimation of hydraulic properties of soils in an outflow experiment. A stochastic, coupled hydrogeophysical inversion was developed, in which the governing equations were solved using the finite volume method and the parameter estimation was conducted using a Bayesian approach associated with the Markov chain Monte Carlo technique. The results show that the addition of SP data in the inversion could reduce the uncertainty related to the estimated hydraulic parameters of soils and the length of the associated 95% confidence interval can be shortened by ∼1/3. It is also shown that the electrical properties of soils at saturated and unsaturated conditions may also be estimated from the outflow experiment when SP data are available. Compared with hydraulic parameters, the accuracy of the estimated electrical properties is slightly lower. Among them, the saturated streaming potential coupling coefficient C_sat has the highest accuracy and lowest uncertainty since C_sat directly influences the magnitude of SP signals. The accuracy of other electrical parameters is lower than that of C_sat (and hydraulic parameters), and the associated uncertainty can be one order of magnitude larger.

中文翻译：

---

来自瞬态流出实验的水力和自电位数据的耦合反演以估计土壤岩石物理特性

土壤的水力学性质对影响临界区降水的分配具有重要作用。除了传统方法之外，在过去的二十年中，许多地球物理方法已被用于帮助地质材料的水文表征和测量。特别是，自电位 (SP) 方法在这些水文地球物理应用中显示出巨大的潜力。本研究的目的是评估添加 SP 数据是否可以改善流出实验中土壤水力特性的估计。开发了一种随机、耦合的水文地球物理反演，其中使用有限体积方法求解控制方程，并使用与马尔可夫链蒙特卡罗技术相关的贝叶斯方法进行参数估计。结果表明，在反演中加入SP数据可以减少与估计土壤水力参数相关的不确定性，相关的95%置信区间的长度可以缩短～1/3。还表明，当 SP 数据可用时，也可以从流出实验估计饱和和非饱和条件下土壤的电特性。与水力参数相比，估计的电气特性的精度略低。其中，饱和流势耦合系数 还表明，当 SP 数据可用时，也可以从流出实验估计饱和和非饱和条件下土壤的电特性。与水力参数相比，估计的电气特性的精度略低。其中，饱和流势耦合系数 还表明，当 SP 数据可用时，也可以从流出实验估计饱和和非饱和条件下土壤的电特性。与水力参数相比，估计的电气特性的精度略低。其中，饱和流势耦合系数C _sat具有最高的准确度和最低的不确定性，因为C _sat直接影响 SP 信号的幅度。其他电气参数的精度低于C _sat（和水力参数），相关的不确定性可能大一个数量级。