Building　reflectivity　and　quality　factor　(Q)　using　nonstationary　poststack　seismic　data　is　important　for　vertical　resolution　enhancement　of　seismic　data　and　reservoir　identification.　However,　it　is　well-known　that　both　reflectivity　and　Q　affect　the　waveform　of　seismic　data,　leading　to　the　fact　that　simultaneously　estimating　them　is　a　strong　ill-posed　multiparameter　inverse　problem　which　faces　the　crosstalk　problem.　In　this　article,　we　propose　a　new　method　for　simultaneous　inversion　of　reflectivity　and　Q.　A　deep-learning-based　data　decoupling　operator　is　proposed　to　decouple　the　effects　of　the　two　parameters　on　nonstationary　seismic　data.　Based　on　the　decoupled　data,　we　transform　the　original　multiparameter　inverse　problem　into　two　independent　singe-parameter　inverse　problems　that　are　immune　to　crosstalk　and　can　build　reasonable　initial　models　for　reflectivity　and　Q.　Then　alternative　iteration　is　conducted　to　update　the　two　built　initial　models　to　obtain　the　final　models.　A　few　well-logs　are　used　to　train　the　deep-learning　architecture　and　specific　regularization　terms　are　constructed　for　the　inverse　problem　to　ensure　physically　reasonable　results.　Synthetic　and　field　data　examples　verify　the　effectiveness　of　the　proposed　method　and　its　advantages　over　a　conventional　model-driven　joint　inversion　method.