Bayesian inversion of GPR waveforms for sub-surface material characterization: An uncertainty-aware retrieval of soil moisture and overlaying biomass properties

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. • Probabilistic prediction of sub-surface parameters using GPR waveform inversion. • Simultaneous estimation of permittivity, conductivity, soil moisture, and depth. • Soil moisture can be predicted in the presence of an overlaying organic layer. • Overlaying organic layer can be characterized for wildfire risk assessment. • Probabilistic uncertainty can be leveraged to identify inconsistent predictions.