A hybrid sparrow optimization Kriging model and its application in geological modeling

Area of Science:

• Geological Engineering
• Artificial Intelligence
• Data Science

Background:

• Intelligent mines require extensive geological data for 3D modeling.
• Interpolation of limited drilling data is crucial for accurate geological representation.
• Existing methods face challenges in accuracy and efficiency for complex geological formations.

Purpose of the Study:

• To develop a novel hybrid sparrow optimization Kriging model (HSSA) for improved geological data interpolation.
• To enhance the accuracy and convergence speed of geological data interpolation techniques.
• To support the creation of precise 3D stratigraphic models for mining operations.

Main Methods:

• Integration of chaos theory and Levy flight into the sparrow algorithm's population and location updates.
• Incorporation of the golden sine optimization algorithm to enhance reconnaissance and early warning mechanisms.
• Correlation optimization of the sparrow algorithm and solving Kriging variation function parameters to reduce interpolation errors.

Main Results:

• The HSSA demonstrated superior accuracy and convergence speed compared to other swarm intelligence algorithms.
• Accuracy improved by 8.4% compared to the original Kriging interpolation algorithm.
• A 3D stratigraphic model for Yangchangwan Coal Mine was successfully generated using HSSA, validating its applicability.

Conclusions:

• The HSSA significantly enhances geological data interpolation accuracy and efficiency.
• The model provides a robust tool for 3D stratigraphic modeling in coal mining and geological exploration.
• HSSA shows substantial potential for optimizing geological interpretation and operational workflows in related industries.