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Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We present methods to detect potential pre-earthquake anomalies. In particular, we show the analysis of lithospheric, atmospheric, and ionospheric data and the detection of anomalies under specific criteria. When we apply these methods retrospectively, we find that their accuracy goes from 69% to 83%. The combination of two or more methods is expected to improve the accuracy. [ABSTRACT FROM AUTHOR]

Earthquake prediction is the holy grail of seismology and one of humanity's greatest dreams. The Earth's magnetic field appears to be one of the best possible precursors of earthquakes, although the topic is controversial. Recent advancements have made it possible to observe magnetic fields from satellites with great accuracy. We utilize magnetic measurements from Swarm satellites to explore the potential identification of anomalous magnetic signals preceding earthquakes. Focusing on 1077 major earthquakes that occurred in 2014–2023 in the Alpine–Himalayan belt, we apply an automatic algorithm to data recorded 10 days before each earthquake. This analysis reveals clear pre-earthquake anomalies in the magnetic field components. Notably, a robust correlation is established between the duration of these anomalies and the earthquake magnitude, indicating that as the earthquake magnitude increases, so does the duration of the anomaly. Here we show that this method has a great ability to make predictions (high accuracy 79%, precision 88%, F1-score and hit rate 84%), thus becoming the basis for an Operational Earthquake Prediction System (OEPS). [ABSTRACT FROM AUTHOR]