Complex systems in geophysics: methods of research and prediction of their behavior
DOI:
https://doi.org/10.24028/gj.v47i1.312874Keywords:
complex system, earthquake, forecasting, artificial intelligence, neural networksAbstract
Many natural and man-made systems have recently been considered and studied from the perspective of complex systems. Complex systems are systems formed by a large number of components that interact with each other, usually nonlinearly. As they evolve, complex systems can self-organize and acquire fundamentally new properties not inherent to the constituents.
This review focuses on geosystems, which are natural complex systems. Most part of the article highlight the properties of such complex geosystems as seismically active areas. Itconsiders the criteria by which systems can be classified as complex, their main properties, and possible methods of studying and predicting their behavior. The review analyzes various models for climate forecasts and models that describe seismic processes originating from the complex behavior of lithospheric subsystems (seismically active zones). Most phenomenological regularities that describe the statistical properties of earthquakes are large-scale, indicating the complexity of this system. Forecasting earthquakes remains the most important task for seismological research despite rather modest achievements. The intensity of research in this direction does not decrease, since the consequences of earthquakes for humanity are significant. The types of forecasts and models used for forecasting are analyzed. Special attention is paid to modern forecasting methods that use artificial intelligence. Various approaches for forecasting seismic events, their advantages, and disadvantages,as well as the difficulties that arise in forecasting tasks, are described. The science of complex systems is rapidly developing and it has great prospects for acquiring one of the most important tools for studying the surrounding natural environment and man-made artificial systems.
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