Accurate estimation of sub-surface properties like moisture content and depth of 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 surface material, making its characterization using conventional methods challenging. In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire assessment. This study thus proposes a Bayesian model-updating-based approach for ground penetrating radar (GPR) waveform inversion to predict sub-surface properties like the moisture contents and depths of the soil layer and overlaying material accumulated above the soil. The dielectric permittivity of material 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 and depth of the top layer (or overlaying material), and 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 top layer depth could also be predicted with reasonable accuracy. 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.

翻译：精确估计地下属性（如含水量和地层深度）对于地下状态监测、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地表覆盖层覆盖，这使得使用传统方法对其表征变得困难。此外，覆盖层属性的估计对于野火评估等应用同样关键。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，用于预测地下属性，如土壤层及覆盖层材料（累积于土壤之上）的含水量和深度。所提方法预测了材料层的介电常数，以及包括深度和电导率在内的其他参数。该贝叶斯模型更新方法能够给出这些参数的概率估计，从而提供与估计值相关的置信度和不确定性信息。该方法通过实验室和现场调查收集的多样化实验数据进行了评估。实验室调查中涵盖了土壤含水量和顶层（或覆盖材料）深度的变化，而现场调查则包含连续十六天的田间土壤含水量测量。结果表明，预测结果与时域反射仪（TDR）测量结果和常规称重法测试结果具有一致性。同时，顶层深度也得以合理精确地预测。所提方法为面向不确定性感知的地下参数估计提供了一种有前景的方案，可支持广泛应用于风险评估中的决策制定。