A Poisson Process Hidden Markov Cellular Automata Model in Earthquake Genesis and Conflict Analysis: A Physical Approach

Pushan Kumar Dutta, O.P. Mishra, Mrinal Kanti Naskar

Abstract


All large-scale events, like natural disasters and conflict mechanisms follow certain endogenous and exogenous interactions, which are still unclear to geoscientists by normal surface observations. It has been established that complex scale analysis provides a direct insight into the precursory framework for occurrence of endogenous and exogenous events related to large-scale dynamics. The present study is based on the mathematical derivation of a theoretical framework for the process in Earth system dynamics using Poisson process and Markov analysis to identify the endogenous and exogenous stress distribution and their redistributions beneath the sub-surface earth. The transition probability matrix derived for the optimal state sequences of the Markov model, which is relevant in analysis of shock wave of a complex system. This study validates the concept of exogenous events using sand pile cellular automata system, an approach to study complex behavior of multicomponent system. In this paper, Poisson hidden Markov model implemented on a continuous space of sand pile behavior shows that seismogenesis and conflicts occur due to accumulating stress, representing disequilibria in energy and interactions between active agents (faults and heterogeneities) in which the stress may find its release through the onset of a tremor. Earthquake occurrence in the critical state or a critical shock due to the contributor states or conflict in societal analysis is not adequately known. Subsequently, the influence of each contributor tries to come back into stasis (meta-stable state or stable equilibrium state), the spatial system position of earth dynamics or conflicts through a series of tremors (aftershocks) or post-earthquake responses.


Keywords


Endogenous And Exogenous Interactions; Earthquake Genesis; Conflict; Avalanche; Emergence; Poisson; Markov; Transition Probability Matrix

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