Inversion of Self Potential Anomaly for Coal Seam Fire Prediction Using Genetic Algorithm
P. Singh, S.K. Pal and S. Kumar
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Near Surface Technologies A
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 721.33Kb )
Price: € 20
Coal seam fire is a challenging issue and many methods and techniques has been developed for its mapping and prediction. The paper discusses the use of an evolutionary algorithm, Genetic Algorithm (GA) on Self-Potential (SP) data for the prediction of coal seam fire. The GA used here is real encoded and is based on Rayleigh Crossover technique. To check the efficiency of algorithm we used GA on synthetic data to characterize the parameters of buried sphere, cylinder and 2D thin sheet. After successfully determining the parameters of synthetic causative bodies, the algorithm was implemented on SP field data of Chattabad Colliery of Jharia Coal field, India. The SP anomaly recorded over the regions of coal fire is mainly due to thermoelectric effect which arise due to the effect of temperature gradient observed in the coal fires. The inverted results helped us in determining the depth of the fire regions as well as other geometrical factors to characterize the fire regions.