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Accurate estimation of sub-surface properties such as moisture content anddepth of soil and vegetation layers is crucial for applications spanningsub-surface condition monitoring, precision agriculture, and effective wildfirerisk assessment. Soil in nature is often covered by overlaying vegetation andsurface organic material, making its characterization challenging. In addition,the estimation of the properties of the overlaying layer is crucial forapplications like wildfire risk assessment. This study thus proposes a Bayesianmodel-updating-based approach for ground penetrating radar (GPR) waveforminversion to predict moisture contents and depths of soil and overlayingmaterial layer. Due to its high correlation with moisture contents, thedielectric permittivity of both layers were predicted with the proposed method,along with other parameters, including depth and electrical conductivity oflayers. The proposed Bayesian model updating approach yields probabilisticestimates of these parameters that can provide information about the confidenceand uncertainty related to the estimates. The methodology was evaluated for adiverse range of experimental data collected through laboratory and fieldinvestigations. Laboratory investigations included variations in soil moisturevalues, depth of the overlaying surface layer, and coarseness of its material.The field investigation included measurement of field soil moisture for sixteendays. The results demonstrated predictions consistent with time-domainreflectometry (TDR) measurements and conventional gravimetric tests. The depthof the surface layer could also be predicted with reasonable accuracy. Theproposed method provides a promising approach for uncertainty-aware sub-surfaceparameter estimation that can enable decision-making for risk assessment acrossa wide range of applications.

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