Accurate estimation of sub-surface properties such as moisture content and depth of soil and vegetation layers is crucial for applications spanning sub-surface condition monitoring, precision agriculture, and effective wildfire risk assessment. Soil in nature is often covered by overlaying vegetation and surface organic material, making its characterization challenging. In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire risk assessment. This study thus proposes a Bayesian model-updating-based approach for ground penetrating radar (GPR) waveform inversion to predict moisture contents and depths of soil and overlaying material layer. Due to its high correlation with moisture contents, the dielectric permittivity of both layers were predicted with the proposed method, along with other parameters, including depth and electrical conductivity of layers. The proposed Bayesian model updating approach yields probabilistic estimates of these parameters that can provide information about the confidence and uncertainty related to the estimates. The methodology was evaluated for a diverse range of experimental data collected through laboratory and field investigations. Laboratory investigations included variations in soil moisture values, depth of the overlaying surface layer, and coarseness of surface material. The field investigation included measurement of field soil moisture for sixteen days. The results demonstrated predictions consistent with time-domain reflectometry (TDR) measurements and conventional gravimetric tests. The correlation coefficient between predicted soil permittivity and that measured by TDR ranged from 0.84 to 0.99 for the presence of up to 10 cm of overlaying surface layer. The depth of the surface layer could also be accurately predicted for up to 10 cm, while at higher depths, prediction outliers could be reasonably identified from the obtained probability distributions and uncertainty. Hence, the proposed method provides a promising approach for uncertainty-aware sub-surface parameter estimation that can enable decision-making for risk assessment across a wide range of applications.

中文翻译：

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用于地下材料表征的探地雷达波形的贝叶斯反演：土壤湿度和覆盖生物量特性的不确定性感知检索


准确估计土壤和植被层的含水量和深度等地下特性对于地下条件监测、精准农业和有效野火风险评估等应用至关重要。自然界中的土壤通常被覆盖的植被和表面有机物质覆盖，这使得其表征具有挑战性。此外，覆盖层特性的估计对于野火风险评估等应用至关重要。因此，本研究提出了一种基于贝叶斯模型更新的探地雷达（GPR）波形反演方法，以预测土壤和覆盖材料层的水分含量和深度。由于其与水分含量高度相关，因此使用所提出的方法预测了两层的介电常数以及其他参数，包括层的深度和电导率。所提出的贝叶斯模型更新方法产生这些参数的概率估计，可以提供有关与估计相关的置信度和不确定性的信息。该方法通过实验室和现场调查收集的各种实验数据进行了评估。实验室调查包括土壤湿度值的变化、覆盖表层的深度以及表面材料的粗糙度。现场调查包括测量十六天的田间土壤湿度。结果证明预测与时域反射计 (TDR) 测量和传统重量测试一致。对于覆盖表面层高达 10 厘米的情况，预测的土壤介电常数与 TDR 测量的介电常数之间的相关系数范围为 0.84 至 0.99。 表层的深度也可以准确预测到10厘米，而在更深的深度，可以从获得的概率分布和不确定性中合理地识别预测异常值。因此，所提出的方法为不确定性感知的地下参数估计提供了一种有前途的方法，可以实现跨广泛应用的风险评估决策。