ENTROPY METHODS OF SELF-ORGANIZING SYSTEMS MODELING USING HYPERSPECTRAL REMOTE SENSING DATA

Abstract

Various mathematical models are created for exploring complex self-organizing systems. In geosystems, the deterministic nature of processes is due to their stochastic properties. In such systems, regular deterministic processes are formed by numerous random interelement interactions that occur at the micro level. In many cases, it is not possible to define correctly the deterministic law of evolution of the observable system or its part because of the large number of unpredictable and unknown factors that influence it. However, at the micro level, statistical distributions of system elements are available for observation, which allows to predict its behavior and evaluate the factors influencing the system. The most universal methods for modeling systems with stochastic properties are based on the fundamental concepts of statistical mechanics — the Gibbs-Shannon and Renyi information entropies. The article studies the entropy methods for calculating quantitative estimations of the states of spatially distributed geosystems and their divergences in the process of self-organization: alpha-divergence, Kullback divergence, variability of the spectrum of Renyi dimensions. The features of the above systems with multifractal structures according to hyperspectral measurements are observed. The examples illustrate the use of entropy models in numerical experiments with real data obtained from a natural gas field. Verification of the entropy methods for determining the boundaries of hydrocarbon deposits based on the hyperspectral data of emission of a homogeneous vegetation cover was carried out.