Simulation Method and Application of Non-Stationary Random Fields for Deeply Dependent Seabed Soil Parameters

Abstract

The spatial variability of geotechnical parameters, such as soil shear wave velocity (Vs), exhibits significant nonlinearity and non-stationarity with respect to depth (h) due to the influence of overlying stress. Existing stochastic field models for describing the variability of geotechnical parameters are insufficient for simultaneously capturing both the nonlinearity and non-stationarity of these parameters. In this study, a power function Vs = Vs0[f(h)]n is proposed to describe the nonlinear trend in soil shear wave velocity (Vs) as a function of depth-related variable f(h). Considering the physical significance and correlation of the power function parameters Vs0 and n, the variability of these parameters is modeled using a random variable model and a stationary stochastic field model, respectively. This leads to the development of a non-stationary stochastic field model that describes the spatial variability of Vs. The proposed method is applied to simulate the random Vs-structure of a seabed site in China, and the obtained Vs results are used to assess the liquefaction probability of the seabed. The results indicate that ignoring the correlation between geotechnical parameters significantly increases the variability of the final simulation results. However, the proposed method accurately captures the nonlinear trend and non-stationary characteristics of soil Vs with depth, and the liquefaction probability predictions are consistent with those derived from in situ Vs measurements in the study area. This approach provides valuable guidance for simulating the spatial variability of depth-dependent geotechnical parameters, particularly those significantly influenced by overlying pressure.