Exact 3D seismic data reconstruction using Tubal-Alt-Min algorithm

TL;DR

This paper introduces Tubal-Alt-Min, a fast tensor completion algorithm that effectively reconstructs 3D seismic data by leveraging low-tubal-rank properties, outperforming existing methods in accuracy.

Contribution

The paper proposes a novel low-tubal-rank tensor model and the Tubal-Alt-Min algorithm for efficient 3D seismic data reconstruction, demonstrating significant improvements over tensor nuclear norm minimization.

Findings

Tubal-Alt-Min reduces reconstruction error by orders of magnitude.

The method performs well on both synthetic and real seismic data.

It is faster and more accurate than existing tensor completion algorithms.

Abstract

Data missing is an common issue in seismic data, and many methods have been proposed to solve it. In this paper, we present the low-tubal-rank tensor model and a novel tensor completion algorithm to recover 3D seismic data. This is a fast iterative algorithm, called Tubal-Alt-Min which completes our 3D seismic data by exploiting the low-tubal-rank property expressed as the product of two much smaller tensors. TubalAlt-Min alternates between estimating those two tensor using least squares minimization. We evaluate its reconstruction performance both on synthetic seismic data and land data survey. The experimental results show that compared with the tensor nuclear norm minimization algorithm, Tubal-Alt-Min improves the reconstruction error by orders of magnitude.