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

Strong earthquakes (magnitude ≥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 the powerful mathematical tool of 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 and northern Mexico, 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 (Formula presented.) 8 is 4.1 years and for strong earthquakes 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 ± 5 and 2057 ± 5, 2024 ± 1 and 2026 ± 1, 2026 ± 2 and 2031 ± 2, 2024 ± 2 and 2029 ± 2, and 2022 ± 1 and 2028 ± 2 for the five active seismic zones of United States, Mexico, South America, Japan, and Southern China and Northern India, respectively. In additon, our methodology can be applied in areas where moderate earthquakes occur, as for the case of the Parkfield section of the San Andreas fault (California, United States). Our methodology explains why a moderate earthquake could never occur in 1988 ± 5 as proposed and why the long-awaited Parkfield earthquake event occurred in 2004. Furthermore, our model predicts that possible seismic events may occur between 2019 and 2031, with a high probability of earthquake events at Parkfield around 2025 ± 2 years.Fil: Velasco Herrera, Victor Manuel. Universidad Nacional Autónoma de México; MéxicoFil: Rossello, Eduardo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Orgeira, Maria Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Arioni, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Soon, Willie. Center for Environmental Research and Earth Sciences; Estados UnidosFil: Velasco, Graciela. Universidad Nacional Autónoma de México; MéxicoFil: Rosique de la Cruz, Laura. Universidad Nacional Autónoma de México; MéxicoFil: Zúñiga, Emmanuel. Universidad Nacional Autónoma de México; MéxicoFil: Vera, Carlos. Universidad Nacional Autónoma de México; Méxic

A New Long-Term Marine Biodiversity Monitoring Program for the Knowledge and Management in Marine Protected Areas of the Mexican Caribbean

In the Mexican Caribbean, 15 marine protected areas (MPAs) have been established for managing and protecting marine ecosystems. These MPAs receive high anthropogenic pressure from coastal development, tourism, and fishing, all in synergy with climate change. To contribute to the MPAs’ effectiveness, it is necessary to provide a long-term observation system of the condition of marine ecosystems and species. Our study proposes the establishment of a new marine biodiversity monitoring program (MBMP) focusing on three MPAs of the Mexican Caribbean. Five conservation objects (COs) were defined (coral reefs, seagrass beds, mangroves, marine turtles, and sharks-rays) for their ecological relevance and the pressures they are facing. Coral reef, seagrass and mangroves have multiple biological, biogeochemical and physical interactions. Marine turtles are listed as endangered species, and the status of their populations is unknown in the marine area of the MPAs. Elasmobranchs play a key role as top and medium predators, and their populations have been poorly studied. Indicators were proposed for monitoring each CO. As a technological innovation, all information obtained from the MBMP will be uploaded to the Coastal Marine Information and Analysis System (SIMAR), a public, user-friendly and interactive web platform that allows for automatic data management and processing

Puerto Morelos Coral Reefs, Their Current State and Classification by a Scoring System

Marine protected areas have been established as essential components for managing and protecting coral reefs to mitigate natural and anthropogenic stressors. One noteworthy example within the Mexican Caribbean is the Arrecife de Puerto Morelos National Park (APMNP), where several studies on the coral communities have been carried out since 2006. In June 2019, we conducted a study in eight sites of the APMNP applying a coral reef assessment method based on biological indicators of both the benthos and the fish communities. In this paper, we present the quantitative results of our study and provide a qualitative criterion assessing seven condition indexes through a scoring system. We also present a statistical comparison with a previous study carried out in 2016. The general status of coral reefs was classified as regular due to the low values of coral recruitment rate and biomass of key commercial fish species. However, living coral cover average was above 20%, with a slight dominance of framework building coral species and the presence of low values of fleshy algae cover, these being positive indicators. Our study found a higher proportion of reef promoter elements and a lower proportion of detractors, compared to a previous study carried out in 2016