Monitoring subsidence around the Raniganj coalfield by integrated GPS and PSInSAR approaches

Abstract

The Raniganj coalfield of West Bengal, India, has been producing non-coking coal of exceptional quality for 246 years, making it the oldest coal mine in India. In this coal mining region, the "room and pillar" mining method is employed to dig coal, resulting in local subsidence that comprises a number of huge subsidence ponds and minor area collapses. Subsidence significantly impacts the environment, resulting in a cataclysmic disaster that destroys public infrastructure and even causes death. The majority of land subsidence is produced by forces that apply pressure on the surface, such as groundwater extraction, mining, excessive development, and similar reasons. A very precise Differential Global Positioning System (DGPS), which is an enhanced GNSS, is used to monitor the movement of the ground. There are nine rover stations and three base stations, which are reoccupied at an interval of months and installed for hours. Since 2019, the DGPS has been used for this purpose. In addition, Synthetic Aperture Radar (SAR) images have been employed to provide a bird's-eye perspective, thereby employing the PS-InSAR method. PS-InSAR (Persistent Scatterer Interferometry) has shown its ability to detect ground subsidence in vegetated and rural areas. In addition, it resolves persistent scatterers with low spatial density by recognising and collecting data from partially correlated scatterers, or PSs. We observed subsidence in the study area from 2017 to 2022, omitting the monsoon months (June to September), using publicly available Sentinel-1 data from the ESA (European Space Agency). Consequently, we discovered that the LOS velocity varies between -7.4 and +5.3 mm/year, as reported by ascending datasets, and between -8.7 and +6.1 mm/year, as reported by descending datasets. Along with a scope of comparative study of the results obtained from DGPS and PS-InSAR, the rate of subsidence in the old underground mining region reveals that some densely populated areas are at risk.