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Abstract Valdivia Bank (VB) is an oceanic plateau in the South Atlantic that formed from hotspot‐ridge volcanism during the Late Cretaceous at the Mid‐Atlantic Ridge (MAR). It is part of Walvis Ridge (WR), a quasi‐linear seamount chain extending from offshore Namibia to Tristan da Cunha and Gough Islands. To understand Valdivia Bank evolution, we interpret the seismic stratigraphy from multichannel seismic data paired with coring results from International Ocean Discovery Program (IODP) Expedition 391, which recovered mostly pelagic nannofossil ooze and chalks. The seismic section can be divided into three seismic units (SU), a lower transparent interval which is faulted and conforms to basement, a middle, moderate to high amplitude interval which is thick in local depocenters such as rifts, and an upper, subparallel transparent interval. Notable features include regional unconformities, dipping clinoforms, mass transport and contourite deposits, and volcanic structures. Additionally, three infilled rifts are observed across the plateau. Our analysis implies that following a period of sedimentation in the Campanian, the edifice was faulted through the Paleocene, coinciding with a South Atlantic tectonic reorganization. Local depocenters formed as a result of rifting. Subsequently, the plateau experienced thermal rejuvenation and regional uplift during the Eocene. Volcanic mounds were emplaced atop Cretaceous sediments and intrusives were emplaced within the sediments. During the Cenozoic, sedimentation was punctuated, likely in response to changes in the carbonate compensation depth and bottom current intensification. VB sedimentation was complex and largely influenced by the paleoceanographic context of the plateau, as well as thermal rejuvenation and tectonism. 

Reconstruction of sparsely sampled seismic data is critical for maintaining the quality of seismic images when significant numbers of shots and receivers are missing.We present a reconstruction method in the shot-receiver-time (SRT) domain based on a residual U-Net machine learning architecture, for seismic data acquired in a sparse 2-D acquisition and name it SRT2D-ResU-Net. The SRT domain retains a high level of seismic signal connectivity, which is likely the main data feature that the reconstructing algorithms rely on. We develop an “in situ training and prediction” workflow by dividing the acquisition area into two nonoverlapping subareas: a training subarea for establishing the network model using regularly sampled data and a testing subarea for reconstructing the sparsely sampled data using the trained model. To establish a reference base for analyzing the changes in data features over the study area, and quantifying the reconstructed seismic data, we devise a baseline reference using a tiny portion of the field data. The baselines are properly spaced and excluded from the training and reconstruction processes. The results on a field marine data set show that the SRT2D-ResU-Net can effectively learn the features of seismic data in the training process, and the average correlation between the reconstructed missing traces and the true answers is over 85%.