Seismic data interpolation based on U-net with texture loss

TL;DR

This paper introduces SUIT, a U-net based method that improves seismic data interpolation by preserving texture information, leading to more accurate reconstruction of missing traces in seismic datasets.

Contribution

The study proposes a novel seismic data interpolation approach that incorporates texture preservation using a pre-trained U-net, enhancing reconstruction quality over existing methods.

Findings

Outperforms state-of-the-art methods in robustness

Preserves seismic texture information effectively

Reduces reconstruction error significantly

Abstract

Missing traces in acquired seismic data is a common occurrence during the collection of seismic data. Deep neural network (DNN) has shown considerable promise in restoring incomplete seismic data. However, several DNN-based approaches ignore the specific characteristics of seismic data itself, and only focus on reducing the difference between the recovered and the original signals. In this study, a novel Seismic U-net InterpolaTor (SUIT) is proposed to preserve the seismic texture information while reconstructing the missing traces. Aside from minimizing the reconstruction error, SUIT enhances the texture consistency between the recovery and the original completely seismic data, by designing a pre-trained U-Net to extract the texture information. The experiments show that our method outperforms the classic state-of-art methods in terms of robustness.