Tunnel three-dimensional geological uncertainty intelligent modeling method and system based on transition probability geostatistics and sparse drilling

By combining transition probability geostatistics with sparse boreholes, the smoothing problem of geological models under sparse boreholes in tunnel engineering was solved, the rationality and uncertainty of three-dimensional lithology models were quantified, and the scientific nature of risk assessment and design in tunnel engineering was improved.

CN122289576APending Publication Date: 2026-06-26SOUTHWEST JIAOTONG UNIV
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Patent Information

Application Number
CN202610186151.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-09
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

In tunnel engineering, existing technologies struggle to construct three-dimensional geological models that conform to geological sequence patterns and quantify uncertainties under sparse drilling conditions. This results in overly smooth modeling results or neglect of stratigraphic order patterns, failing to meet engineering requirements under complex geological conditions.

Method used

By employing a transition probability-based geostatistical approach, sparse borehole data and geological prior knowledge are integrated. Through a one-dimensional transition probability matrix and a three-dimensional variogram model, combined with sequential indicator simulation and Bayesian updates, a multi-realization three-dimensional lithology model is generated, and the uncertainty is quantified.

Benefits of technology

By generating a three-dimensional lithological model with a reasonable geological structure and conforming to sedimentary logic under sparse drilling conditions, the uncertainty of the model can be quantitatively represented in space, thereby improving the scientificity and reliability of risk assessment and design in tunnel engineering.

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Abstract

The invention relates to the technical field of tunnel engineering and three-dimensional geological modeling, in particular to a tunnel three-dimensional geological uncertainty intelligent modeling method and system based on transition probability geostatistics and sparse drilling, and the method comprises the steps: S1, integrating multi-source data, and constructing a three-dimensional geological conceptual model; s2, statistically depicting a one-dimensional transition probability matrix, and calculating and fitting a spatial continuity and three-dimensional anisotropy variation function model; s3, calculating a prior space probability and a transfer adjustment factor of each type of lithology, obtaining node posterior lithology distribution through Bayesian intelligent updating, and performing random sampling to preliminarily endow node lithology categories; s4, aiming at the grid nodes, endowing the grid nodes with most possible lithologic categories, calculating variances of lithologic values in all implementation, and measuring the uncertainty of the model; s5, outputting a tunnel three-dimensional uncertainty optimal model; and S6, verifying and evaluating. According to the method, the three-dimensional heterogeneous modeling and uncertainty explicit quantification under the strong constraint of the geological law can be better realized.
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