Researchers have discovered that the predictability of large earthquakes may be better than previously believed. This revelation comes from a study that utilized machine learning to analyze decades of past earthquakes in California.
Various methods have been attempted in the past to predict earthquakes using real-world signals, such as monitoring changing water levels, or analyzing data catalogues that track the time intervals between earthquakes. However, none of these methods have proven to be consistently reliable.
In the 1970s, a prominent seismological hypothesis emerged suggesting that earthquake predictability could be based on the sequence of earthquakes in a given region. However, this hypothesis has not been consistently supported by evidence.
It is worth noting that this article was amended on August 30, 2022, to correct the size of the area that was modeled in the earthquake risk assessments.
Insights:
- Machine learning techniques are being employed to analyze historical earthquake data in order to improve earthquake predictability.
- Previous methods of predicting earthquakes based on real-world signals and data catalogues have not yielded consistently reliable results.
- A hypothesis proposed in the 1970s suggested that earthquake predictability could be based on the sequence of earthquakes in a given region but has not been consistently supported by evidence.
