Deep-learning-based　methods　have　been　successfully　applied　to　seismic　data　random　noise　attenuation.　Among　them,　the　supervised　deep-learning-based　methods　dominate　the　unsupervised　ones.　The　supervised　methods　need　accurate　noise-free　data　as　training　labels.　However,　the　field　seismic　data　cannot　meet　this　requirement.　To　circumvent　it,　some　researchers　utilized　realistic-looking　synthetic　data　or　denoised　results　via　conventional　methods　as　labels.　The　former　ones　encounter　the　problem　of　weak　generalization　ability　because　it　requires　the　same　distribution　of　test　and　training　data.　The　latter　ones　encounter　the　issue　of　insufficient　denoising　ability　because　its　denoising　ability　is　difficult　to　significantly　exceed　the　conventional　methods　which　were　used　to　generate　labels.　To　avoid　preparing　noise-free　labels,　we　propose　a　novel　deep　learning　framework　for　attenuating　random　noise　of　prestack　seismic　data　in　an　unsupervised　manner.　The　prestack　seismic　data,　such　as　common-reflection-point　(CRP)　gathers　and　common-midpoint　(CMP)　gathers　after　normal　moveout　(NMO)　correction,　have　high　self-similarity.　It　is　because　their　events　are　coherent　in　the　timex2013;space　domain　and　approximately　horizontal　from　shallow　to　deep　layers.　The　generator　convolutional　neural　network　(GCN)　first　learns　self-similar　features　before　any　learning.　The　useful　signals　are　more　self-similar　than　random　noise,　which　is　incoherent　and　randomly　distributed.　Therefore,　the　GCN　extracts　features　of　useful　signals　before　random　noise.　We　select　the　specific　training　iteration　and　adopt　the　early　stopping　strategy　to　suppress　random　noise.　Both　synthetic　and　field　prestack　seismic　data　examples　demonstrate　the　validity　of　our　methods.