With　the　escalating　development　and　utilization　of　subterranean　spaces,　the　seismic　hazards　faced　by　underground　structures　are　progressively　increasing.　However,　owing　to　the　challenges　associated　with　acquiring　underground　seismic　data　and　historical　seismic　design　norms,　pertinent　regulations　and　research　in　this　domain　are　scarce.　This　study　focused　on　three　crucial　intensity　measures　in　the　seismic　design　process　of　underground　structures　peak　ground　acceleration　(PGA),　peak　ground　velocity　(PGV),　and　peak　ground　displacement　(PGD).　The　research　leverages　seismic　data　obtained　from　the　California　Strong　Motion　Instrumentation　Program　(CSMIP)　to　train　and　evaluate　a　conditional　generative　adversarial　network　(CGAN)　model.　This　model　was　employed　to　establish　a　multivariate　joint　conditional　probability　distribution　among　the　intensity　measures　at　varying　depths,　facilitating　the　stochastic　prediction　of　shallow　intensity　measures.　In　contrast　to　empirical　formulas,　the　CGAN　model　eliminates　the　need　for　a　predefined　equation　structure　and　enables　the　simultaneous　prediction　of　multiple　intensity　measures.　The　performance　of　the　model　was　evaluated　by　comparing　the　predictive　accuracy　of　the　CGAN　model　and　empirical　fitting　formulas　across　diverse　site　conditions　and　depth　intervals　using　metrics　such　as　relative　error　coefficients.　It　can　be　concluded　that　the　proposed　CGAN　model　can　accurately　predict　shallow　seismic　intensity　measures,　and　the　predictions　conform　to　a　specific　conditional　distribution　while　retaining　the　stochastic　nature　of　seismic　motion.　Compared　with　empirical　formula　models,　the　CGAN　model　exhibited　an　enhanced　predictive　capability.　©　2024　Elsevier　Ltd