A new study by researchers at the University of Tsukuba examined the role of solar heat in earthquake activity. By analyzing seasonal variations in seismicity, correlations between solar activity and earthquakes, and the influence of atmospheric temperature on earthquake predictability, the study provides new insights into how solar-driven thermal effects may contribute to stress changes in the Earth’s lithosphere.
Researchers have developed a new machine learning and neural network model with a detailed feature matrix to improve earthquake prediction accuracy in Los Angeles, achieving a 97.97% success rate.
A new study was conducted to understand the precursors to the 2021 Fukushima Prefecture Offshore Earthquake (Mj = 7.3), which occurred on February 13, 2021, off the coast of Fukushima, Japan, as an aftershock of the 2011 Tōhoku earthquake and tsunami. The investigation, led by Masashi Hayakawa and Yasuhide Hobara, focused on the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) channels and multi-parameter anomalies that occurred before the earthquake.
A recent study by Társilo Girona of the University of Alaska Fairbanks and Kyriaki Drymoni of Ludwig-Maximilians-Universität in Munich proposed a new machine learning technique for predicting big earthquakes months in advance. The study, published in Nature Communications on August 28, 2024, intended to improve earthquake predictions and public safety.
A recent study challenges the prevailing hypothesis that subducted seamounts are weakly coupled and slide aseismically, suggesting instead that they act as strong asperities causing significant earthquakes. Through modeling and analysis, researchers have debunked the weak asperity model, providing a new explanation for the source of historical tsunami earthquakes along the southern Japan Trench, including the 1677 M8.3–8.6 Enpo Boso-oki event.
Researchers at the International Centre for Earth Simulation Foundation, led by Max Wyss, have developed the Earthquake Fatality Load (EQFL), a new metric for assessing the impact of earthquakes by focusing on human fatalities relative to a country’s population.
Researchers from Penn State and Brown University have uncovered how rocks from ancient subduction zones can forecast tectonic behaviors between significant earthquakes, enhancing our ability to predict such natural disasters.
A new study published in the journal Science could be a game-changer in the field of seismology, as researchers have discovered a potential missing piece in the puzzle of predicting earthquakes. The study suggests that ultralow frictional healing, a common physical phenomenon, could be crucial in understanding when and how violently faults move. While it won’t enable scientists to forecast the next big earthquake, it could be a valuable new way to investigate the causes and potential for large, damaging earthquakes.
Researchers reporting in AGU’s JGR Solid Earth identified changes in the magnetic field near intermediate-large earthquakes in California in the days before the earthquakes happened, providing evidence that there is a physical change that can be observed in the days before an earthquake.
A new study published recently in Remote Sensing proposes the implementation of machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes.
