The　noise　horizontal-to-vertical　spectral　ratio　(NHV)　has　been　applied　for　inverting　subsurface　velocity　structures,　but　the　non-uniqueness　issue　in　the　inversion　remains　prominent.　Parameter　sensitivity　analysis　is　crucial　for　understanding　the　extent　to　which　parameters　influence　inversion　results,　providing　reasonable　value　ranges　for　each　parameter,　and　offering　rational　constraints　to　mitigate　the　non-uniqueness　of　solutions.　This　study　takes　636　sites　from　the　KiK-net　network　as　benchmark　sites　and　employs　the　Monte　Carlo　method　based　on　Toro＇s　statistical　model　to　generate　200　random　samples　for　each　site＇s　shear　wave　velocity　(V-S),　compressional　wave　velocity　(V-P),　layer　thickness　(h),　and　density　(rho)　parameters.　These　random　samples　are　then　combined　into　six　scenarios,　serving　as　the　stochastic　site　models　for　uncertainty　analysis.　Finally,　based　on　the　diffuse　field　assumption　(DFA)　for　NHV　forward　modeling,　the　NHVs　and　their　standard　deviations　are　calculated　for　each　scenario.　The　standard　deviations　of　NHV　are　further　utilized　to　conduct　a　sensitivity　analysis　of　soil　layer　parameter　uncertainties＇　impact　on　NHV.　The　results　indicate:　1)　The　NHV　peak　frequency　(f(peak))　is　most　sensitive　to　the　V-S　and　h　combination,　followed　by　the　V-S　and　rho　combination.　Among　single-parameter　scenarios,　fpeak　is　most　sensitive　to　V-S,　followed　by　h,　and　relatively　insensitive　to　V(P　)and　rho.　2)　The　NHV　peak　amplitude　(Apeak)　is　most　sensitive　to　the　V-S　and　rho　combination,　followed　by　the　V(S　)and　h　combination.　Among　singleparameter　scenarios,　Apeak　is　most　sensitive　to　V-S,　approaching　the　sensitivity　level　of　the　V-S　and　h　combination,　followed　by　rho,　and　relatively　insensitive　to　h　and　V-P.　3)　Within　the　0.1-50　Hz　frequency　band,　the　NHV　curve　is　most　sensitive　to　the　V-S　and　h　combination,　followed　by　the　V-S　and　rho　combination,　and　the　singleparameter　V-S　scenario.　Among　single-parameter　scenarios,　the　NHV　curve　is　secondly　sensitive　to　h,　while　its　sensitivity　to　V-P　and　rho　is　relatively　low.　The　sensitivity　patterns　of　NHV　to　soil　parameters　revealed　in　this　study　are　widely　applicable,　providing　effective　constraints　for　NHV　inversion,　assisting　in　optimizing　parameter　combinations,　assessing　the　feasibility　of　inversion　schemes　and　the　credibility　of　results,　and　offering　beneficial　insights　for　improving　the　efficiency　of　the　inversion　process.