# Seismic data interpolation based on U-net with texture loss

arXiv: https://arxiv.org/abs/1911.04092 · HTML: https://mlsanity.com/paper/1911.04092/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [StorSeismic: A new paradigm in deep learning for seismic processing](https://mlsanity.com/paper/2205.00222/) (2022)
- [Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling](https://mlsanity.com/paper/2307.04226/) (2023)
- [MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network](https://mlsanity.com/paper/2209.03132/) (2022)
- [Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network](https://mlsanity.com/paper/2205.03645/) (2022)
- [Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data](https://mlsanity.com/paper/2602.01909/) (2026)

## Abstract (arXiv)

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.