AUTHOR=Velasco Herrera Victor Manuel , Rossello Eduardo Antonio , Orgeira Maria Julia , Arioni Lucas , Soon Willie , Velasco Graciela , Rosique-de la Cruz Laura , Zúñiga Emmanuel , Vera Carlos 

TITLE=Long-Term Forecasting of Strong Earthquakes in North America, South America, Japan, Southern China and Northern India With Machine Learning

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.905792

DOI=10.3389/feart.2022.905792

ISSN=2296-6463

ABSTRACT=Strong earthquakes (M ≥ 7) occur worldwide affecting different cities and countries while causing great human, ecological and economic losses. The ability to forecast strong earthquakes on the long-term basis is essential to minimize the risks and vulnerabilities of people living in highly active seismic areas. We have studied seismic activities in North America, South America, Japan, Southern China and Northern India in search for patterns in strong earthquakes on each of these active seismic zones between 1900 and 2021 with wavelet transform. We found that the primary seismic activity patterns for M ≥ 7 earthquakes are 55, 3.7, 7.7, and 8.6 years, for seismic zones of the southwestern United States, southwestern Mexico, South American, and Southern China-Northern India, respectively. In the case of Japan, the most important seismic pattern for earthquakes with magnitude 7 ≤ M < 8 is 4.1 years and for strong earthquake with M ≥ 8, it is 40 years. Every seismic pattern obtained clusters the earthquakes in historical intervals/episodes with and without strong earthquakes in the individually analyzed seismic zones. We want to clarify that the intervals where no strong earthquakes do not imply the total absence of seismic activity because earthquakes can occur with lesser magnitude within this same interval. From the information and pattern we obtained from the wavelet analyses, we created a probabilistic, long-term earthquake prediction model for each seismic zone using the Bayesian Machine Learning method. We propose that the periods of occurrence of earthquakes in each seismic zone analyzed could be interpreted as the period in which the stress builds up on different planes of a fault, until this energy releases through the rupture along faults and fractures near the plate tectonic boundaries. Then a series of earthquakes can occur along the fault until the stress subsides and a new cycle begins. Our machine learning models predict a new period of strong earthquakes between 2040 and 2057, 2024 and 2026, 2026 and 2031, 2024 and 2029, and 2022 and 2028 for the five acive seismic zones of the USA, Mexico, South America, Japan, and Southern China and Northern India, respectively.