Complex Valued Markov Random Field (CMRF) Based Monte Carlo Metropolis (MCM) for Monitoring Surface Deformation

The faulting of surface in earth surface deformation related with relative displacements of adj acent phases of surface. However, consideration lack of deformation state transition causes errors in prediction outcomes of surface deformation through Artificial Intelligence (AI) techniques. In this research, proposed a Complex valued Markov Random Field (CMRF) based Monte Carlo Metropolis (MCM) for monitoring surface deformation. The Interferometric Synthetic Aperture Radar (InSAR) images is developed as CMRF. The corrupted phases that are represented through residues in phase information ae restored through MCM. Proposed method produced high prediction outcomes with less error rates. The performance of proposed method is evaluated with performance measures of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed method attained less MAPE of 1.82, MAE of 0.51, MSE of 0.39 and RMSE of 0.032 which is superior than other existing methods like Spatio-Temporal Association Rule Mining (STARM) and Probability Integral Method (PIM)