Spatiotemporal prediction of mining-induced surface subsidence using integrated D-InSAR and boltzmann time function...

Area of Science:

• Geosciences
• Remote Sensing
• Mining Engineering

Background:

• Underground coal mining expansion necessitates precise surface subsidence prediction.
• Differential Interferometric Synthetic Aperture Radar (D-InSAR) and the Boltzmann function are key tools for monitoring ground deformation.

Purpose of the Study:

• To develop and validate a hybrid framework for accurate spatiotemporal prediction of surface subsidence dynamics.
• To integrate D-InSAR, Boltzmann function, and Probability Integral Method (PIM) for comprehensive subsidence modeling.

Main Methods:

• A hybrid framework coupling D-InSAR time-series data with the Boltzmann function for low-gradient zones.
• Combining D-InSAR edge constraints and leveling data to invert PIM parameters in high-gradient zones, then fusing with Boltzmann parameters.
• Analyzing quantitative relationships between Boltzmann parameters and influencing factors like maximum subsidence, mining rate, and overburden lithology.

Main Results:

• The hybrid framework demonstrated stable dynamic prediction performance with relative errors between 2.1% and 7.0% at the 2301 working face.
• Quantitative relationships were established between Boltzmann parameters and influencing factors.
• The model enables continuous subsidence prediction across entire mining basins.

Conclusions:

• The proposed hybrid framework offers a scalable and adaptable solution for dynamic subsidence prediction in underground coal mining.
• This approach provides effective technical support for mining-induced subsidence management and ecological restoration.
• Integrating remote sensing, time-dependent functions, and spatial modeling enhances subsidence prediction accuracy and reliability.