Construction of knowledge constraints: a case study of 3D structural modeling

Area of Science:

• Geological Sciences
• Computer Science
• Artificial Intelligence

Background:

• Data-driven geological modeling faces challenges due to structural interpretation uncertainties.
• Existing methods struggle with complex geological structures, hindering practical application.

Purpose of the Study:

• To develop an intelligent structural modeling approach using knowledge-guided methods.
• To enhance the accuracy and reliability of complex geological structure modeling.

Main Methods:

• Utilizing knowledge rule reasoning to extract topological semantic knowledge from interpretive data.
• Employing a rule-based knowledge inference system for deriving structural constraints.
• Representing extracted knowledge as a topological semantic knowledge graph for computer recognition.

Main Results:

• Successfully constrained the 3D geological structure modeling process through derived structural information.
• Enabled accurate estimation of intersection lines during 3D geological modeling.
• Validated the method's applicability across diverse complex geological structures using real-world data.

Conclusions:

• The proposed knowledge-guided method effectively addresses uncertainties in geological interpretation.
• The approach facilitates intelligent structure modeling in real-world geological working areas.
• This enhances the practical production and application of complex geological structure modeling technology.