A method and system for generating sccrf

Abstract

The invention provides an SCCRF (structural cross constraint random field) generation method and system. The method utilizes parameters of actual sampling data as multi-dimensional constraint data, and generates a new random field by considering the cross correlation between rock and soil spatial structure and different variables. According to the method, the actual observation data is taken as the known point position condition data, and unknown point data is generated by considering the crossover among a plurality of variables through synergistic Kriging. The hard data is constrained by thesoft data while the original regionalized variable random field is generated, and then the random field considering the correlation among the regionalized variables is obtained. At the same time, thecomplete random field and the co-Kriging method are combined to consider the directional complex anisotropic spatial variation structure, and then the structural cross constraint random field is generated. The generation method reproduces the second-order statistical characteristics of the research area parameters, considers the random field simulation of the complex anisotropic spatial variationstructure type, and improves the assignment precision.

Description

technical field [0001] The present invention relates to the field of geological data analysis, and more particularly, to a method and system for generating SCCRF. Background technique [0002] Due to the influence of factors such as material composition, depositional conditions, geological tectonic movement, and internal and external dynamic geological action, rock and soil mass forms different spatial structures in space, showing local randomness and overall structure. That is, the physical and mechanical parameters of rock and soil have strong spatial variability (Rahardjo et al., 1995; Zhang et al., 1996). The structure of spatial variation of rock and soil parameters is the spatial variation structure of parameters, including the type of spatial variation (isotropic or anisotropic), the degree of spatial variation and the orientation of spatial variation. As the main source of randomness in engineering, the variability of geotechnical parameters has always been an impor...

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Problems solved by technology

The current disadvantages of this method mainly lie in: ①The current generation of constrained random fields is mostly researched on isotropic and orthotropic spatial variation structures. For directional complex anisotropic spatial variation structures, especially those with The spatial variation structure of shape anisotropy is less involved; ②The calculation process of the existing constrained random field simulation method is relatively complicated and the calculation efficiency is low

For example, the Bayes method is greatly affected by the prior statistical information of the parameters, and the constrained random field generated by the Markov chain Monte Carlo simulation method has a low acceptance probability for multi-parameter (multi-dimensional) samples and a large amount of calculation; ③ During the generation process of the random field Without comprehensive consideration of the cross influence of multi-dimensional parameters, it is generally based on single-parameter variable discrete random fields (Wu Zhenjun et al., 2009; Deng Zhiping, 2014; Zhang Shu et al., 2017)

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