Method and apparatus for dynamic quantification of seepage risk

CN121835522BActive Publication Date: 2026-05-26NORTHWEST ENGINEERING CORPORATION LIMITED
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHWEST ENGINEERING CORPORATION LIMITED
Filing Date
2026-03-16
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Traditional seepage risk assessment methods struggle to accurately characterize the spatial heterogeneity of seepage parameters, and the risk prediction results are static and singular, lacking quantitative expression of parameter errors and linkage updates of construction information, resulting in inaccurate prediction results and limited decision support capabilities.

Method used

A deterministic spatial trend field of seepage parameters is constructed using a multi-scale geographically weighted regression model. A stochastic residual field is obtained by combining Kriging interpolation. Finally, a Bayesian assimilation algorithm is used to fuse deterministic and stochastic information to dynamically quantify seepage risk.

Benefits of technology

It improves the accuracy and stability of seepage risk indicator prediction, can dynamically update risk levels, and enhances the reliability and interpretability of risk warning and construction decisions.

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Abstract

This invention provides a method and apparatus for dynamically quantifying seepage risk, relating to the field of engineering prediction technology. The method includes: acquiring observational data of seepage parameters and geological environmental factors related to seepage parameters at different observation points in a target area; constructing a multi-scale geographically weighted regression model based on the observational data of seepage parameters and the geological environmental factors to obtain a deterministic spatial trend field of seepage parameters; calculating the spatial residuals of the observed seepage parameters relative to the deterministic spatial trend field, and performing Kriging interpolation on the spatial residuals to obtain a stochastic residual field and uncertainty information; fusing the deterministic spatial trend field and the stochastic residual field to obtain a spatial distribution field of seepage parameters; and using a Bayesian assimilation algorithm based on the spatial distribution field of seepage parameters and uncertainty information to obtain the dynamic quantification result of seepage risk. This invention can solve the core defects of traditional methods, such as insufficient characterization of the spatial heterogeneity of seepage parameters and static and singular risk prediction.
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