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The analysis of waves in the visible side of the Sun allows the detection ofactive regions in the farside through local helioseismology techniques. Theknowledge of the magnetism in the whole Sun, including the non-visiblehemisphere, is fundamental for several space weather forecasting applications.Seismic identification of farside active regions is challenged by the reducedsignal-to-noise, and only large and strong active regions can be reliabledetected. Here we develop a new methodology to improve the identification ofactive region signatures in farside seismic maps. We have constructed a deepneural network that associates the farside seismic maps obtained fromhelioseismic holography with the probability of presence of active regions inthe farside. The network has been trained with pairs of helioseismic phaseshift maps and Helioseismic and Magnetic Imager magnetograms acquired half asolar rotation later, which were used as a proxy for the presence of activeregions in the farside. The method has been validated using a set of artificialdata, and it has also been applied to actual solar observations during theperiod of minimum activity of the solar cycle 24. Our approach shows a highersensitivity to the presence of farside active regions than standard methodsapplied up to date. The neural network can significantly increase the number ofdetected farside active regions, and will potentially improve the applicationof farside seismology to space weather forecasting.

Improved detection of farside solar active regions using deep learning