Noise　attenuation　is　a　very　important　step　in　seismic　data　processing,　which　facilitates　accurate　geologic　interpretation.　Random　noise　is　one　of　the　main　factors　that　lead　to　reductions　in　the　signal-to-noise　ratio　(SNR)　of　seismic　data.　It　is　necessary　for　seismic　data,　including　complex　geological　structures,　to　develop　a　number　of　new　noise　attenuation　technologies.　In　this　article,　we　concern　with　a　new　variational　regularization　method　for　random　noise　attenuation　of　seismic　data.　Considering　that　seismic　reflection　events　often　have　spatially　varying　directions,　we　first　employ　the　gradient　structure　tensor　(GST)　to　estimate　the　spatially　varying　dips　point　by　point　and　propose　the　structure-oriented　directional　total　generalized　variation　(DTGV)　(SODTGV)　functional.　Then,　we　employ　the　SODTGV　as　a　regularizer　to　establish　an　$\ell　_{2}$　-SODTGV　model　and　develop　the　primal-dual　algorithm　for　solving　this　model.　Next,　the　choice　of　the　model　parameters　is　discussed.　Finally,　the　proposed　model　is　applied　to　restore　noisy　synthetic　and　field　data　to　verify　the　effectiveness　of　the　proposed　workflow.　For　contrastive　methods,　we　select　the　structure　adaptive　median　filtering　(SAMF),　anisotropic　total　variation　(ATV),　total　generalized　variation　(TGV),　DTGV,　median　filtering,　KL　transform,　SVD　transform,　and　curvelet　transform.　The　synthetic　and　real　seismic　data　examples　indicate　that　our　proposed　method　can　preferably　improve　the　vertical　resolution　of　seismic　profiles,　enhance　the　lateral　continuity　of　reflection　events,　and　preserve　local　geologic　features　while　improving　the　SNR.　Moreover,　the　proposed　regularization　method　can　also　be　applied　to　other　inverse　problems,　such　as　image　processing,　medical　imaging,　and　remote　sensing.