Particle swarm optimization inversion of self-potential data for depth estimation of coal fires over East Basuria colliery, Jharia coalfield, India

Coal fires pose a serious threat to the environment and it is important to detect them at an early stage for their control and hazard mitigation. The present study addresses an innovative approach for depth estimation of coal fires using self-potential (SP) method and its inversion through particle swarm optimization (PSO) technique. The suitability of PSO inversion technique for self-potential data has been established using synthetic models of spherical and cylindrical objects, and inclined sheet with large horizontal extent as causative sources. Present study reveals that the geometry of subsurface coal combustion is possibly similar to inclined sheet with relatively large horizontal extension. The depth of coal fires has been estimated using PSO inversion of SP anomaly data over the East Basuria colliery, Jharia coal field, Jharkhand, India with good accuracy. The results of the analysis are compared with borehole lithologic log data which proves efficacy of the PSO inversion technique.