Seismic data processing algorithms greatly benefit from regularly sampled and reliable data.
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …

ABSTRACT A common issue of seismic data analysis consists in the lack of regular and
densely sampled seismic traces. This problem is commonly tackled by rank optimization or …

Despite recent developments in improved acquisition, seismic data often remain
undersampled along source and receiver coordinates, resulting in incomplete data for key …

Seismic data acquired by geophones are processed to estimate images of the earth's
interior that are used to explore, develop, and monitor resources and to study the shallow …

A properly designed skip-connection convolutional autoencoder deep generator is able to
capture the inner structure of shot gathers from subsampled seismic data without any pre …

Acquired seismic data is normally not the fully sampled data we would like to work with since
traces are missing due to physical constraints or budget limitations. Rank minimization is an …

Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for
data interpolation allow practitioners to “fill in” data volumes from critically subsampled data …

Low-rank matrix completion techniques have recently become an effective tool for seismic
trace interpolation problems. In this talk, we consider an alternating optimization scheme for …

Introduction. Seismic data are often sparsely or irregularly sampled along one or more
spatial axes. Irregular sampling can produce artifacts in seismic imaging results, thus …

A fundamental aspect of modern seismic data processing involves the reconstruction of
wavefields in areas where missing sources or receivers result in data gaps. Despite recent …