Geophysics-informed stratigraphic modeling using spatial sequential Bayesian updating algorithm

Challenges in stratigraphic modeling arise from underground uncertainty. While borehole exploration is reliable, it remains sparse due to economic and site constraints. Electrical resistivity tomography (ERT) as a cost-effective geophysical technique can acquire high-density data; however, uncertainty and non-uniqueness inherent in ERT impede its usage for stratigraphy identification. This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles. The method consists of two steps: (1) ERT for prior knowledge: ERT data are processed by soft clustering using the Gaussian mixture model, followed by probability smoothing to quantify its depth-dependent uncertainty; and (2) Observations for calibration: a spatial sequential Bayesian updating (SSBU) algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations, namely topsoil and boreholes. The effectiveness of the proposed method is validated through its application to a real slope site in Foshan, China. Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling, in terms of prediction accuracy at borehole locations and sensitivity to borehole data. Informed by ERT, reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements. The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements, the impact of model resolution, and applicability in engineering projects. This study, as a breakthrough in stratigraphic modeling, bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.