A new model seeks to improve the prediction of earthquakes. This is by considering, in greater detail, the full history of a fault’s earthquakes in order to forecast when the next one might occur.
The main challenge is that earthquakes have a strong degree of unpredictability, sometime occurring as single events and at other times in clusters. Essential to the new predictive power is an understanding of the so-termed ‘leftover strain’ from an earthquake, where the degree of this can help to predict whether a sooner-than-average second earthquake will occur.
An earthquake is the shaking of the surface of the Earth resulting from a sudden release of energy in the Earth’s lithosphere (the solid, outer part of Earth) that creates seismic waves.
The new model comes from Northwestern University. Here researchers have been looking at arguably seismology’s most important problem: when to expect the next big earthquake on a fault.
In the earlier days of seismology, it was theorised that big earthquakes on faults are regular and that the next quake will occur after approximately the same amount of time as between the previous two. Unfortunately, data and real life events do not follow this pattern. The older model assumes that a fault has only short-term memory, that is, it “remembers” only the last earthquake and has “forgotten” all the previous ones.
A greater reliability of predictions arises from the new research. Here seismologists and statisticians has developed an earthquake probability model. This new model considers the specific order and timing of previous earthquakes. Through this, the model helps explain the puzzling fact that earthquakes sometimes come in clusters (groups with relatively short times between them) and which are then separated by longer times without earthquakes.
According to lead researcher Seth Stein: “Considering the full earthquake history, rather than just the average over time and the time since the last one, will help us a lot in forecasting when future earthquakes will happen.”
Drawing on a sporting analogy, Stein elaborates: “When you’re trying to figure out a team’s chances of winning a ball game, you don’t want to look only at the last game and the long-term average. Looking back over additional recent games can also be helpful. We now can do a similar thing for earthquakes.”
More recent studies show that large earthquakes do not occur like clockwork. The new model assess the long-term fault memory which exists because an earthquake may not have released all the strain that built up on the fault over time, so some remains after a big earthquake and can cause another. This explains earthquakes that sometimes come in clusters.
To test out the model, the scientists applied their model to the southern San Andreas fault as an example and show how the model can produce better forecasts when clusters and gaps are present.
Hence, the specific order and timing of past earthquakes matters. Many systems’ behaviour depends on their history over a long time, and earthquakes appear to be no exception.
The research appears in the journal Bulletin of the Seismological Society of America, titled “A More Realistic Earthquake Probability Model Using Long-Term Fault Memory.”
