The 'critical points' of earthquake prediction
A new discovery has been made which should help us to better understand the complex nature of earthquakes. A physicist at the Universitat Autònoma de Barcelona, Spain, has discovered that the structure of the recurrence time of earthquakes, which is the time interval between successive quakes, is similar to the spatial structure of physics systems when they change phase in the 'critical points'. The research, led by Alvaro Corral, is published in Physical Review Letters and shows that the time interval between successive earthquakes depends on the time that elapsed between previous earthquakes. Although this is dependent upon the availability of statistics, the discovery may help to improve risk estimation. Examples of critical phenomena in nature include when water changes state, moving from liquid to gaseous form, and when a magnet is at the critical point, where it loses its magnetism because due to high temperatures. In the second example, the magnet exhibits a property known as 'self-similarity across scales', which exists only at the moment when it changes state. When the temperature is below the critical point, the microscopic magnets that form the magnetic fields are well ordered and point mainly in the same direction. When the temperature rises above the critical point, everything becomes chaotic, each microscopic magnet points in a random direction, and there is no global magnetic field. When the temperature is at the critical point, on the borderline, the microscopic magnets that point in the same direction are grouped together in small clusters. If one zooms in and look at a smaller area, these clusters are actually grouped in clusters of clusters, and the same thing occurs each time we zoom out to look at a larger area. This property is known as self-similarity across scales. There are different types of self-similarity: exact self-similarity normally only occurs in mathematically defined fractals, where the normal realities or constraints on structures by the physical world don't apply. A far more common type of self-similarity is an approximate one, that is, as one looks at the object at different scales one sees structures that are recognisably similar but not exactly so. This is the case of the self-similarity found in fern leaves: here, self-similarity occurs, but is limited to a certain range and at a few discrete scales. Finally, sometimes the self-similarity is isn't visually obvious but there may be numerical or statistical measures that are preserved across scales. This is the type of self-similarity discovered by the UAB researchers: self-similarity at different scales occurring in the time intervals between earthquakes. This discovery means that if we note the different earthquakes that have taken place in a given zone over a long period of time, it is possible to see that they are grouped together. More surprisingly, if one looks at a longer period of time, the groups of earthquakes themselves are also grouped into larger clusters. The same happens for any period of time, for earthquakes of any magnitude, wherever they take place in the world. This has fundamental implications regarding the type of phenomenon that earthquakes are. Rather than being chaotic, as one might think, we can consider them to be critical. As Dr Corral confirmed: 'For this self-similar structure to exist, the role of correlations between earthquakes must be very important, that is, the interval between earthquakes must be dependant on previous earthquakes in a very determined way.' Dr. Corral stresses that this dependence is not determinist. That is, his theory does not allow for predictions of when earthquakes will occur, but the clear statistical dependence may certainly help to improve risk estimation.
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