Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer...

Area of Science:

• Geosciences
• Remote Sensing
• Mining Engineering

Background:

• Mine operational safety is crucial for uninterrupted mining activities.
• Land surface deformation poses risks to mining infrastructure and personnel.
• Accurate monitoring of surface changes is essential for risk assessment and mitigation.

Purpose of the Study:

• To analyze land surface changes and deformation at the Musan mine using InSAR time series.
• To enhance the spatial coverage and quality of deformation analysis through the ICOPS method.
• To improve the reliability of InSAR results by incorporating machine learning.

Main Methods:

• Utilized the improved combined scatterers with optimized point scatters (ICOPS) method for InSAR analysis.
• Combined persistent scatterers (PS) and distributed scatterers (DS) to improve deformation analysis.
• Applied machine learning, specifically convolutional neural networks, for postprocessing and reliability enhancement.

Main Results:

• Detected significant land subsidence with an average rate exceeding 15.00 cm/year.
• Measured total surface deformation of 170 cm in the eastern dumping area and 70 cm in the western dumping area.
• Correlated land surface changes with geological conditions in the Musan mining area.

Conclusions:

• The ICOPS method, enhanced with machine learning, effectively monitors mine surface deformation.
• Significant subsidence rates highlight potential risks to mine operational safety.
• Combining machine learning and statistical methods offers a powerful approach for understanding mine surface deformation.