In the heart of China’s coal mining industry, a groundbreaking method is emerging to tackle one of the sector’s most persistent challenges: mining subsidence. This phenomenon, where the ground sinks due to underground mining activities, poses significant risks to infrastructure, safety, and operational efficiency. Now, a team of researchers led by Ding Xingcheng from the College of Geoscience and Surveying Engineering at China University of Mining and Technology-Beijing, has developed a novel approach that combines advanced satellite technology and innovative optimization algorithms to predict and monitor mining subsidence with unprecedented accuracy.
The method, detailed in a recent study published in the Journal of Mining Science, integrates the probability integral method with SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) technology. This fusion of techniques allows for a more comprehensive and precise assessment of subsidence in mining areas.
At the core of this innovation is the dung beetle optimizer algorithm, a cutting-edge optimization tool known for its robustness and high accuracy. Ding Xingcheng explains, “The dung beetle optimizer algorithm helps us invert the parameters of the probability integral method more effectively, especially in areas with large deformation gradients. This leads to more reliable subsidence predictions.”
The process begins with SBAS-InSAR technology, which uses satellite data to monitor ground deformation. This technology provides reliable subsidence values for areas with small deformation gradients. The dung beetle optimizer algorithm then steps in to refine these values, particularly in regions with significant ground movement. Finally, the subsidence data from both methods are fused using a quadratic distance weighting approach, resulting in a comprehensive and accurate subsidence map for the mining area.
The practical implications of this research are immense. Accurate subsidence prediction can significantly enhance safety measures, reduce operational costs, and minimize environmental impact. For the energy sector, which relies heavily on coal mining, this technology could revolutionize how subsidence is managed, leading to more sustainable and efficient mining practices.
The study’s findings are compelling. When applied to the 10604 working face of the Malan Mine in Gujiao City, Shanxi Province, the new method improved accuracy by 59% compared to using SBAS-InSAR alone and by 32% compared to the probability integral method alone. This dramatic improvement underscores the potential of this integrated approach.
Ding Xingcheng’s work, published in the Journal of Mining Science, which is known in English as the Journal of Mining Science, represents a significant leap forward in mining subsidence management. As the energy sector continues to evolve, such innovations will be crucial in ensuring that mining operations are not only profitable but also safe and environmentally responsible.
The implications of this research extend beyond China, offering a blueprint for mining operations worldwide. As the global demand for coal and other minerals remains high, the need for accurate subsidence prediction and monitoring will only grow. This method, with its blend of advanced technology and innovative algorithms, could very well shape the future of the mining industry, making it safer, more efficient, and more sustainable.
