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A research team from the New Jersey Institute of Technology in the United States has developed a machine learning model called EarlyDetect, based on the Transformer architecture. By analyzing solar observation data from NASA's Solar Dynamics Observatory HMI, combined with acoustic power maps and magnetic field measurements, the model can identify precursors to the formation of solar active regions an average of 9.24 hours in advance. The underlying principle may be that as the Sun's internal magnetic field rises, it leaves traces in the propagation of acoustic waves. Currently, the model has not yet been put into practical use. If further validated, it could provide satellite communication companies and power grid operators with longer lead times to prepare for the risks posed by solar storms.
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