Detection of coal fires at an early stage is very important for its control and mitigation operations. Coal fires induce fractures and cracks in the subsurface which accelerate the rate of combustion. In this work we demonstrate the usage of a novel approach in detecting coal fires and delineating these fractures using Self-Potential surveys and Particle Swarm Optimization inversion. The study area is take in the East Basuria colliery, Jharia Coal Field, Jharkhand, India and inversion results are compared with litho logs drilled in the region. The inversion scheme was tested on synthetic SP response in the presence of random noise. For the case study the causative source was modelled as inclined sheet like anomalies for SP survey undertaken in the region. The results demonstrate the robust performance of the algorithm and illustrates the usage of this novel approach in detecting coal fires for control and mitigation operations.


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