A new study shows that we might be able to predict big earthquakes in the near future better than we thought. Attempts were made in the past to predict earthquakes by using signals such as changing water levels or time intervals between earthquakes.
Now, John Rundle at the University of California and his team have developed an algorithm that gives the probability of an earthquake of magnitude 6.75 or greater happening at various times over a three year period in a region of California. The model is based on records of earthquakes since 1970 and indicates that past earthquake data isn’t random as previously thought.
Instead of looking at the interval between earthquakes, as previous studies have, the team examined the number of earthquakes per unit time and then used a machine learning model to look for patterns in the data. The team then applied statistical methods that are common in economics, such as an exponential moving average, which gives recent events more significance than older ones.
The model gives weather forecast-like probabilities of earthquakes happening in a short, upcoming period of time, such as a 90 percent chance of a major earthquake within three years.
The chance of a greater than magnitude 6.75 earthquake occurring in the region varied over the three years from about 35 per cent to as much as 100 percent. Although the team doesn’t give specific times for an earthquake, the hockey stick shaped pattern of risk the model produces shows the chance of an earthquake rises over time, where the probability quickly tends to certainty. Not long after an earthquake, the risk drops again.