For　an　effective　deep　learning　based　seismic　impedance　inversion　strategy,　a　deep　convolutional　network　is　trained　by　massive　data-driven　models　to　obtain　the　mapping　between　seismic　data　and　impedance.　After　the　network　is　pre-trained　by　substantial　synthetic　data,　a　small　amount　of　real　data　is　required　for　transfer　learning　of　the　network.　In　this　paper,　we　propose　a　new　method　based　on　data　augmentation　and　active　learning　for　seismic　wave　impedance　inversion.　First,　the　original　single-trace　wave　impedance　data　is　augmented　by　resampling　at　the　same　frequency,　and　then　the　reflectivity　and　random　kernel　are　calculated　to　generate　the　seismic　data　after　augmentation.　The　augmented　seismic　and　wave　impedance　data　is　taken　as　training　sets,　and　the　maximum-error　samples　are　selected　to　train　the　network　iteratively　considering　active　learning.　The　proposed　method　can　avoid　seismic　wavelet　estimation,　while　training　the　network　with　higher　accuracy　using　a　small　amount　of　label　data.　The　test　results　from　the　Marmousi　2　model　demonstrate　that　this　method　only　needs　one　tenth　of　label　data　and　iteration　times　to　achieve　the　prediction　accuracy　similar　to　that　of　iterative　random　training,　with　the　prediction　errors　distributed　more　evenly　on　the　profile.　©　2021,　Editorial　Department　OIL　GEOPHYSICAL　PROSPECTING.　All　right　reserved.