New algorithms detect the initial increase in magnitude to help predict large earthquakes

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Many people are aware that large earthquakes are often followed by a series of aftershocks as stresses in the surrounding area are redistributed. Many may not realize that there are also sequences of earthquakes that occur before most large earthquakes.

The so-called precession increment (PSI) describes the sudden increase in the rate and magnitude of earthquakes in a precursor zone, with an earlier time and magnitude before the next major earthquake. Statistical relationships between different precursor variables form the basis of the earthquake prediction model EEPAS (every earthquake is a precursor according to the scale).

EEPAS aims to predict upcoming major earthquakes in the medium term, i.e. within months to decades, depending on their magnitude. EEPAS has performed well in global testing and is an important contributor to New Zealand's overall earthquake prediction and to the New Zealand National Earthquake Hazard Model.

To date, there has been limited analysis of the initial increase in size, likely due to the manual and detailed manner in which it was originally discovered. Recent work has indicated that multiple definitions of PSI can be made for a given earthquake with smaller smoothing regions associated with larger prior times and vice versa.

A more systematic approach to PSI detection was needed to study this trade-off between area and time to confirm that small precursor regions grow as time approaches the main shock. GNS Scientific Hazard and Risk Scientist Dr. Anne-Marie Christoffersen is the lead author of a paper published in Seismological Research Letters that describes two algorithms that automatically detect PSI in earthquake catalogs.

The algorithms have been applied to real earthquake catalogs and to simulated data based on known physics of earthquake occurrence. The algorithms identify multiple PSI probes for most major earthquakes with different times, areas, and magnitudes.

On average, an equal trade-off between time and space is found for both real and synthetic data. Also, the scaling relationships for the PSI parameters are consistent with the original self-specified scaling relationships from which the EEPAS prediction models are derived.

“Our work is critical to enhancing our understanding of how seismic activity evolves towards a major earthquake,” says Dr. Christophersen. “Our next step is to include our findings in the EEPAS model to improve medium-term earthquake prediction, which is a direct input into the overall earthquake forecasting and the National Earthquake Hazard Model. These resources help make better decisions about where to build and prioritize strengthening existing infrastructure to make New Zealand more resilient to large earthquakes.

More information: Annemarie Christophersen et al, Algorithmic identification of ex-scale augmentation in earthquake catalogs, Earthquake Research Letters (2024). doi: 10.1785/0220240233

Citation: New algorithms detect precursory scale increase to help predict large earthquakes (2024, October 11) Retrieved October 11, 2024 from https://phys.org/news/2024-10-algorithms-precursory-scale-big-quakes. html

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