Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spati...Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.展开更多
基金National Key R&D Program of China(No.2018YFA0702504)the National Natural Science Foundation of China(Nos.42074141,41874141)the Strategic Cooperation Technology Projects of CNPC and CUP(ZLZX2020-03).
文摘Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.