Landslide uncertainty model dynamic construction method based on multi-source heterogeneous data fusion

Abstract

The invention provides a landslide uncertainty model dynamic construction method based on multi-source heterogeneous data fusion. The landslide uncertainty model dynamic construction method comprisesthe steps of obtaining spatial variation information of landslide rock-soil mass mechanical parameters through combined application of geological survey, data collection, indoor mechanical tests, CT scanning and numerical sample random reconstruction technologies; constructing a landslide initial uncertainty model on the basis of spatial law statistics of mechanical parameters of the rock-soil body; carrying out landslide numerical simulation under corresponding working conditions in a non-invasive random finite element mode; constructing an intelligent response surface model of landslide output information and random variables at different time points, and updating an uncertainty model in real time by utilizing landslide direct information and monitoring indirect data based on the intelligent response surface model and an adaptive condition sampling reliability Bayesian inversion method, so that a multisource heterogeneous data fused landslide uncertainty model is established. The method has the advantages that a fine landslide geologic model is established, and an effective means is provided for subsequent landslide mechanism analysis and stability evaluation.

Description

technical field [0001] The invention relates to the fields of landslide uncertainty analysis and stability evaluation, in particular to a method for dynamically constructing landslide uncertainty models based on multi-source heterogeneous data fusion. Background technique [0002] The accurate establishment of complex geological-mechanical models is an important basis and premise for carrying out landslide mechanism research and stability evaluation. Under multi-phase and multi-type internal and external dynamic geological actions, the material composition and slope structure of landslides have a large discontinuity. The complex slope structure and the uneven distribution of materials make the rock and soil parameters of different parts of the landslide have randomness, variability and structure. Obviously, considering the complexity of landslide evolution and the randomness, structure and time variability of rock and soil parameters, it is necessary to establish a geologica...

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Problems solved by technology

At present, the establishment of landslide average geological model only uses the previous direct information, and the data utilization rate of geotechnical parameters is low. In the process of establishing the common uncertain model, only direct information or indirect information is used less. The uncertainty of the model is large, and the amount of calculation is large during the model operation, and the real-time dynamic update of the landslide uncertainty model is not comprehensively used.

[0005] The input Bayesian method based on the posterior information can invert and update the probability distribution function of the prior information, and can better establish the relationship between the prior parameter model and various monitoring data. For inversion update, MH-based Markov Chain Monte Carlo (MCMC) is often used to obtain samples that obey the posterior probability distribution, but it is mostly suitable for low-dimensional Bayesian update problem, and the proposed function distribution needs to be given

The prior parameter model can be a simple random variable model or a random field model, and the random field model has high-dimensional variables. The traditional MCMC method cannot effectively solve this problem.

The spatial variation model is combined with landslide engineering through the stochastic finite element method. However, it is time-consuming and computationally intensive to obtain the posterior data under long-period conditions through the finite element numerical model, which limits the update efficiency of the uncertain model. And for the timeliness of monitoring information, it is necessary to continuously update the uncertainty model

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