Using synchronization to improve earthquake forecasting in a cellular automaton model

TL;DR

This paper introduces a novel earthquake forecasting method using synchronization of cloned cellular automaton models, inspired by chaos theory, to predict catastrophic seismic events more effectively.

Contribution

It presents a new forecasting strategy based on anticipated synchronization in cellular automaton models, combining chaos theory and pattern recognition for earthquake prediction.

Findings

Successfully forecasted characteristic earthquakes in the Minimalist Model.

Demonstrated improved prediction accuracy over traditional methods.

Quantitative analysis confirms the effectiveness of synchronization-based forecasting.

Abstract

A new forecasting strategy for stochastic systems is introduced. It is inspired by the concept of anticipated synchronization between pairs of chaotic oscillators, recently developed in the area of Dynamical Systems, and by the earthquake forecasting algorithms in which different pattern recognition functions are used for identifying seismic premonitory phenomena. In the new strategy, copies (clones) of the original system (the master) are defined, and they are driven using rules that tend to synchronize them with the master dynamics. The observation of definite patterns in the state of the clones is the signal for connecting an alarm in the original system that efficiently marks the impending occurrence of a catastrophic event. The power of this method is quantitatively illustrated by forecasting the occurrence of characteristic earthquakes in the so-called Minimalist Model.