Recently,　there　has　been　an　increasing　interest　in　leveraging　physics-informed　neural　networks　(PINNs)　for　modeling　dynamical　systems.　However,　limited　studies　have　been　conducted　along　this　horizon　on　seismic　wave　modeling　tasks.　A　critical　challenge　is　that　these　geophysical　problems　are　typically　defined　in　large　domains　(i.e.,　semi-infinite),　which　leads　to　high　computational　costs.　We　present　a　new　PINN　model　for　seismic　wave　modeling　in　semi-infinite　domain　without　the　need　for　labeled　data.　Specifically,　the　absorbing　boundary　condition　is　introduced　into　the　network　as　a　soft　regularizer　for　handling　truncated　boundaries.　To　scale　up,　we　consider　a　sequential　training　strategy　via　temporal　domain　decomposition　to　improve　the　scalability　of　the　network　and　solution　accuracy.　Moreover,　we　design　a　novel　surrogate　modeling　strategy　to　account　for　parametric　loading,　which　estimates　the　wave　propagation　in　semi-infinite　domain　given　the　seismic　loading　at　different　locations.　Various　numerical　experiments　are　implemented　to　evaluate　the　performance　of　the　proposed　PINN　model　in　the　context　of　forward　modeling　of　seismic　wave　propagation.　In　particular,　we　use　diverse　material　distributions　to　test　the　versatility　of　this　approach.　The　results　demonstrate　excellent　solution　accuracy　under　distinctive　scenarios.