Seismic　blind　high-resolution　inversion　(BHRI)　aims　at　retrieving　the　high-resolution　data　to　characterize　the　stratigraphic　structures　in　the　case　of　an　unknown　seismic　wavelet.　However,　the　unknown　wavelet　and　ill-posedness　pose　a　great　challenge　to　the　high-resolution　inversion.　Regularization-based　BHRI　is　an　effective　approach.　However,　it　is　sensitive　to　the　sets　of　initial　values,　regularization　terms,　and　regularization　parameters　and　suffers　from　computational　burden　problems.　To　address　these　issues,　we　propose　a　sequential　iterative　deep　learning　method　(SIDLM)　to　implement　a　BHRI　in　a　fast　computational　speed,　which　incorporates　three　learned　components　to　sequentially　invert　initial　high-resolution　data,　seismic　wavelet,　and　final　high-resolution　data　in　an　end-to-end　fashion.　Specifically,　to　mitigate　the　influence　of　initial　values,　a　data-driven　network　U-Net　is　adopted　to　learn　an　initial　high-resolution　data.　Furthermore,　the　architecture　makes　use　of　prior　information　encoded　in　the　forward　operator　to　build　a　new　general　alternating　direction　method　of　multipliers　(ADMM)-like　iterative　deep　neural　network,　instead　of　the　traditional　alternating　iterative　inversion.　The　proposed　ADMM-like　network　utilizes　the　convolutional　neural　networks　to　learn　the　proximal　operators　to　solve　each　subproblems　in　alternating　iterative　inversion.　Therefore,　all　parameters　of　BHRI,　such　as　the　regularization　parameters　and　transform　operator,　can　be　implicitly　learned　from　the　training　datasets　in　an　end-to-end　fashion,　not　limited　to　the　form　of　the　penalty　function.　Finally,　the　synthetic　and　field　data　examples　are　conducted　to　demonstrate　the　effectiveness　of　the　proposed　SIDLM.