Improved conditional random field simulation method based on bootstrap- Bayesian inference and its application in identification of seafloor liquefaction

The reasonable determination of correlation distances serves as the prerequisite for ensuring the accuracy of random field simulation results for geotechnical parameters, and also constitutes a critical challenge in random field simulations that remains difficult to resolve. The Bootstrap method was employed to perform resampling on correlation distances. Utilizing the sampling results, a weighted prior probability density function for correlation distances was constructed. By applying Bayesian principles in conjunction with Hoffman's conditional random field simulation method, the decoupling and simultaneous updating of correlation distance determinations and geotechnical parameter estimations in random field simulations were achieved. Taking a seabed site as an example, this study simulated the spatial variability of marine soil SPT-N values and their influence on seabed liquefaction probability. The research revealed the impacts of correlation distances, constraints from measured borehole data, and heterogeneity of original site stratigraphy on random field simulation outcomes and seabed liquefaction probability. The validity of the proposed methodology was confirmed through verification against reserved measurement results at actual borehole locations.