HGMFN:Hierarchical Guided Multicascade Feedback Network for Complex Seismic Data Reconstruction

The seismic data reconstruction techniques are primarily used to address data missing or damaged due to human or environmental factors under restricted acquisition conditions and thus enhance the accuracy of obtained stratigraphic information. Hence, seismic data reconstruction stands as a crucial preprocessing step and is necessary for the effective exploration of subsurface resources. The existing reconstruction methods often fall short of fully utilizing the information existed in seismic data, thereby impacting reconstruction accuracy of effective signals. To overcome the aforementioned limitation, we propose a hierarchical guided multicascade feedback network (HGMFN), which facilitates comprehensive interaction of seismic data across different resolutions by learning intricate features from clean and complete seismic data at various scales. The proposed network achieves progressive integration of features along with layer-by-layer guidance and multilevel feedback mechanisms, accomplishing the reconstruction task and improving the processing precision. Experiments conduct with both synthetic and field data have confirmed the accuracy and performance of HGMFN in complex seismic data reconstruction.