A complex tectonic intelligent prediction system and method integrating geological knowledge graphs and deep learning

CN122311380APending Publication Date: 2026-06-30四川省第七地质大队
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
四川省第七地质大队
Filing Date
2026-03-31
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing geological exploration technologies are unable to accurately predict complex structural features and the risk of roof water inrush, resulting in low operational safety and efficiency. Furthermore, existing methods rely on a single information source or traditional analysis, which cannot fully capture the dynamic changes of multiple coupled factors.

Method used

By integrating geological knowledge graphs and deep learning algorithms, and acquiring real-time exploration datasets, this system analyzes the distribution of formation stress, microseismic activity, and lithological pore structure of aquifers. It constructs a multi-dimensional analysis framework, generates a rule base for the association between exploration structures and water inrush, captures changes in geological parameters in real time, and provides a basis for safety decision-making.

Benefits of technology

It improves the reliability and consistency of prediction results, reduces the probability of roof water inrush accidents, makes full use of data value, reduces reliance on human experience, and enhances exploration safety and efficiency.

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Abstract

This application relates to the field of geological exploration technology, and in particular to an intelligent prediction system and method for complex structures that integrates geological knowledge graphs and deep learning. The method includes: acquiring a real-time exploration dataset of the exploration area; based on the real-time exploration dataset, analyzing the stress distribution trend, microseismic activity rhythm, and aquifer lithology and pore structure under roof water inrush phenomena to obtain a multi-dimensional dynamic feature information set; based on the multi-dimensional dynamic feature information set, constructing a multi-dimensional analysis framework for the direct and indirect interactions between different parameter sets within the multi-dimensional dynamic feature information set; based on the multi-dimensional analysis framework, combining geological knowledge graphs and deep learning algorithms, deriving the complex structural features of the exploration area to obtain dynamic derivation results, generating an exploration structure-water inrush association rule base. Recording roof water inrush risks provides targeted safety decision-making basis for exploration projects, reducing the probability of subsequent water inrush accidents and ensuring the safety of personnel and equipment.
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