Multivariate Statistical approach for assessment of subsidence in Jharia coalfields, India

Indian coalfields, one of the major coal producers, are facing serious problem of subsidence now-a-days. This paper attempts to investigate various factors and their influence on magnitude and extent of subsidence. The study was conducted in the Jharia coalfields, India where extraction of thick seams at shallow depths has damaged the ground surface in the form of subsidence. For precise pre-estimation of subsidence, it is therefore necessary to know the contribution of each factor to the occurrence of subsidence. In order to achieve the objectives of this study, several multivariate statistical techniques such as factor analysis (FA), principal component analysis (PCA) and cluster analysis (CA) have been used. Two factors were extracted using FA. Factor 1 and factor 2 account for 42.327% and 24.661% of the variability respectively. Factor 1 represents “natural factor” whereas factor 2 represents “subsidence coefficient”. Spatial variations in regarding susceptibility to the subsidence were determined from hierarchical CA using the linkage distance. Further, the findings of this study would be helpful for prediction of magnitude of subsidence empirically