Among　the　inversion　methods　for　airborne　transient　electromagnetic　(ATEM)　data,　the　hybrid　inversion　method　integrates　the　iterative　optimization　framework　with　artificial　neural　networks　(ANNs),　ensuring　inversion　accuracy　while　enhancing　the　generalization　capability　of　neural　networks.　However,　this　method　faces　challenges　in　terms　of　slow　computation　speeds　due　to　its　lower　updated　step　length　and　the　lack　of　consideration　for　electromagnetic　response　laws.　Our　method　adopts　a　supervised　descent　method　(SDM)　framework　to　supervise　the　ANNs,　obtaining　a　longer　updated　step　length.　On　the　basis　of　the　SDM　framework,　we　have　considered　the　electromagnetic　response　laws　and　designed　RNN-ResNet　and　1-D-UNet　networks　to　update　the　conductivity　model,　improving　the　computing　speed.　Through　numerical　ablation　experiments,　we　validated　the　effectiveness　of　our　proposed　method　and　compared　the　inversion　results　with　those　obtained　using　the　traditional　hybrid　method.　Additionally,　we　conducted　tests　on　bundle　fringe　distribution,　inversion　fitting　loss,　noise　sensitivity,　and　inversion　speed　for　both　methods　using　measured　data　to　evaluate　their　performance　in　practical　applications.　The　experimental　findings　demonstrate　that　our　method　achieves　the　same　level　of　inversion　accuracy　and　generalization　ability　as　the　traditional　hybrid　method　while　enhancing　inversion　speed　by　up　to　68.5%.