Due　to　the　complex　nature　of　site　conditions　and　the　influence　of　deposition　conditions,　aging,　environmental　exposure,　and　characterization　techniques,　the　calibration　of　geotechnical　parameters　is　significantly　uncertain.　The　present　study　introduces　a　Bayesian　updating　method　for　geotechnical　parameters　to　address　the　issues　of　parameter　uncertainty　and　incomplete　parameter　information.　Developed　by　combining　a　high-fidelity　Polynomial　Chaos　Kriging　(PC-Kriging)　model　with　the　Gibbs　sampling　method,　this　approach　uses　Least　Angle　Regression　(LAR)　to　construct　the　Polynomial　Chaos　Expansion　(PCE)　coefficients,　incorporating　PCE　as　the　trend　function　in　the　Kriging　method　to　build　the　PC-Kriging　model.　The　proposed　method　can　avoid　the　computational　challenges　involved　in　Bayesian　inference　using　dense　numerical　models,　effectively　reducing　computational　costs　while　obtaining　the　posterior　distribution　and　statistical　information　of　the　model.　This　study　primarily　applies　the　proposed　PC-Kriging-Gibbs　(PCK-Gibbs)　method　to　geotechnical　engineering　issues.　The　method　is　validated　on　two　critical　dynamic　soil　problems:　Horizontal-to-vertical　spectral　ratio　(HVSR)　inversion　and　equivalent　linearization　in　site　response　analysis.　Meanwhile,　the　Kriging　method　and　PCE　were　also　used　to　verify　the　feasibility　and　computational　efficiency　of　the　proposed　method.　The　posterior　distribution　samples　of　the　model　parameters　obtained　show　good　consistency　between　the　sample　means　and　actual　values,　significantly　reducing　the　uncertainty　of　shear　wave　velocity.　Compared　to　Bayesian　inference　analysis　using　only　the　Gibbs　method,　the　proposed　method　dramatically　decreases　computation　time　while　maintaining　satisfactory　results,　providing　a　powerful　computational　tool　for　parameter　updating　in　geotechnical　engineering.