This paper introduces a probabilistic approach to significantly improve offshore site characterization from integrated geophysical, geological and geotechnical survey data, and from different technologies used from within each of these disciplines. The proposed Bayesian stratigraphy integration methodology is based on the sequential integration of available evidence (experimental observations, model predictions and experts’ beliefs), which allows for the reduction of uncertainty and improve the quality of geospatial analysis translated into higher stratigraphy resolution and higher confidence on the determination of sediments’ mechanical characteristics. A synthetic case study for a 2D heterogeneous shallow offshore soil media is presented to illustrate the overall methodology. One application of probabilistic cluster identification based on geological data is discussed (e.g. 1D density upscaling profile), as this is then transferred to a probabilistic geophysical inversion, including the corresponding uncertainty propagation and.