## 1 Introduction  
The prediction of a landslide and its stability performance have always been key difficulties of engineering. Particularly for the sensitive and landslide-prone areas in reservoir areas or mountain areas, the water migration inside a landslide is very prominent for its stability [1; 2; 3]. The soil-water characteristic curve (SWCC) is the cornerstone of the seepage calculation of unsaturated soil. In the analysis of saturated-unsaturated seepage, it is very important to fully describe the function of the slope of the moisture content [4; 5]. The preliminary prediction based on the survey data often deviates from the actual monitoring value. As the real conditions are very complicated, the mechanism cannot be fully grasped through analytical models. Therefore, the trial algorithm is often used to repeatedly correct the soil parameters to obtain results that match the monitoring value [6, 7]. The Bayesian method provides an effective way to solve such problems. By using the detection data, a priori information, and model assumptions, the parameter update is realized and the parameter uncertainty is quantified [8, 9]. This technique uses a set of output measurement data (such as a point in a multi-dimensional output space) [10, 11]. The purpose of this paper is to solve the problem of how to check the parameters of a SWCC model of a landslide using the Bayesian updating method. The SWCC is a crucial input for modeling the geotechnical problems with unsaturated soil. It can be describing the relationship between matric suction and volumetric water content in unsaturated soils [12]. The main feature of SWCC is that the slope of the residual water content or saturation function in the pore water pressure range is from negative to positive [13]. As a basic equation in unsaturated soil mechanics, it plays an important role in the application of unsaturated soil mechanics for studying the strengths, deformations, and permeabilities of soils [14]. Past studies have shown that the shear strength of unsaturated soils can be estimated by the SWCC [15, 16, 17]. It can be seen that these parameters play important roles in the safety of geotechnical engineering [18, 19, 20, 21]. Therefore, the SWCC parameters are evaluated as a method to determine the unsaturated soil properties of landslides. In engineering practice, the SWCC is usually measured by field or laboratory tests. The typical methods used are: the tensiometer method, the pressure plate meter method, the cold mirror hygrometer method, and the filter paper method. The limited number of data points are then used to estimate the SWCC by best fitting them with some parametric SWCC models, such as the van Genuchten-Mualem model (VGM), the van Genuchten-Burdine model (VGB), the van Genuchten model (VG), and the Fredlund and Xing model (FX) [14, 22]. This paper adopts the Bayesian updating method, which is essentially a Bayesian statistical reasoning problem [23], to study the uncertainty of the parameters involved in the model. The Bayesian method is a probabilistic method based on the application of Bayesian rules. The resulting solution is called the posterior distribution, which is a multi-dimensional probability distribution function in the update parameter space. The posterior distribution is usually a complex distribution, and in principle it is integral in the parameter space. It can be approximated by statistical methods and using modern algorithms, namely, the Markov chain Monte Carlo (MCMC) method [24]. The distribution model, also known as the maximum posterior probability (MAP), defines the updated model, and the distribution around the model provides a measure of confidence in the model. The peak distribution often means a high degree of confidence in the updated model, and shallowness may make uncertain the choice of parameters [25, 26].