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In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τc and Pd methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (MJMA) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records. [ABSTRACT FROM AUTHOR]

Earthquake swarms are most commonly composed of small‐magnitude earthquakes. However, a recent study by Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) analyzed a swarm beneath the Noto Peninsula in Japan that, after more than two years of moderate‐magnitude seismicity, triggered the moment magnitude (Mw) 6.2 Suzu mainshock in May 2023. Based on high‐precision earthquake locations and a slip inversion of the mainshock, these authors found that the Mw 6.2 Suzu earthquake occurred on the updip extension of a fault that was active during the swarm, likely driven by increased fluid pressure. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby on 1 January 2024. These events underscore the potential for swarms to be precursors to large, damaging earthquakes. Forecasting the eventual evolution of swarms is currently very challenging but could be aided in the future by new observations and models. Plain Language Summary: Although earthquake swarms often only contain small earthquakes, occasionally they can trigger large, damaging earthquakes. Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) examined the triggering of a magnitude 6.2 earthquake in Japan, which caused extensive damage, following two years of smaller earthquakes. This sequence, like many swarms, may have been driven by elevated fluid pressure migrating upwards within the crust. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby. Forecasting the eventual evolution of ongoing swarms is currently very challenging but could be aided in the future by new observations and models. Key Points: Earthquake swarms can be precursors to damaging earthquakes, including the 1 January 2024, Mw 7.5 Noto Peninsula, Japan eventFluid pressure changes often drive swarm activity, and their degree of confinement may influence swarm evolutionDirect observations of fluids in fault zones combined with numerical modeling may aid swarm forecasting in the future [ABSTRACT FROM AUTHOR]