Irregular interpolation of seismic data through low-rank tensor approximation

Seismic data usually show irregular spatial sampling because of cable feathering (for marine acquisitions) or physical obstacles in acquisition area (for land surveys). Seismic traces can be also irregularly distributed because of missing or noisy recordings and sensor faults. In recent literature, matrix and tensor rank optimization have been applied to achieve seismic data interpolation and to attenuate unstructured additive noise. In fact, low-rank components can capture the local features of the recorded data, such as envelope and slopes of the events. In this work, we derive a novel interpolation technique based on the low-rank approximation of matrices and tensors, which can interpolate irregularly sampled seismic data onto an arbitrary output geometry. Results on real data prove the feasibility of the proposed approach.