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Abstract Valdivia Bank (VB) is a Late Cretaceous oceanic plateau formed by volcanism from the Tristan‐Gough hotspot at the Mid‐Atlantic Ridge (MAR). To better understand its origin and evolution, magnetic data were used to generate a magnetic anomaly grid, which was inverted to determine crustal magnetization. The magnetization model reveals quasi‐linear polarity zones crossing the plateau and following expected MAR paleo‐locations, implying formation by seafloor spreading over ∼4 Myr during the formation of anomalies C34n‐C33r. Paleomagnetism and biostratigraphy data from International Ocean Discovery Program Expedition 391 confirm the magnetic interpretation. Anomaly C33r is split into two negative bands, likely by a westward ridge jump. One of these negative anomalies coincides with deep rift valleys, indicating their age and mechanism of formation. These findings imply that VB originated by seafloor spreading‐type volcanism during a plate reorganization, not from a vertical stack of lava flows as expected for a large volcano. 

Abstract Valdivia Bank is an oceanic plateau in the South Atlantic formed by hot spot magmatism at the Mid‐Atlantic Ridge during the Late Cretaceous. It is part of the Walvis Ridge, an aseismic ridge and seamount chain widely considered to be formed by age‐progressive volcanism from the Tristan‐Gough plume. To better understand the formation and history of this edifice, we developed a bathymetric map of Valdivia Bank by merging available multibeam echosounder data sets with a bathymetry grid based mainly on satellite altimetry (SRTM15+). The bathymetric map reveals previously unresolved features including extensive rift grabens, volcanic mounds and knolls, and large‐scale sediment transport systems. After Valdivia Bank was emplaced and probably eroded at sea level, it underwent a period of rifting, followed by a secondary magmatic pulse that caused regional uplift to sea‐level, followed by subsidence to current depths. Shallow banks at depths of ∼1,000 m are the result of a thick sediment pile atop uplifted volcanic crust. Several shallower mounds (∼1,000–520 m) and a guyot (∼220 m) likely resulted from coral reef growth atop one or more volcanic pedestals formed during the younger Cenozoic magmatic event. As sediments accumulated on the shallow platforms, sediment transport systems developed as gullies, channels and mass transport deposits carved valleys and troughs, shedding sediment into abyssal fans at the plateau base. The new bathymetric map demonstrates that oceanic plateaus are geologically active long after initial emplacement. 

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%.