# Interpolation and Denoising of Seismic Data using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1901.07927 · HTML: https://mlsanity.com/paper/1901.07927/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [The potential of self-supervised networks for random noise suppression in seismic data](https://mlsanity.com/paper/2109.07344/) (2021)
- [A hybrid approach to seismic deblending: when physics meets self-supervision](https://mlsanity.com/paper/2205.15395/) (2022)
- [Deep learning for inverse problems with unknown operator](https://mlsanity.com/paper/2108.02744/) (2021)
- [Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion](https://mlsanity.com/paper/2102.13631/) (2021)
- [Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs](https://mlsanity.com/paper/2105.11622/) (2021)
- [PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks](https://mlsanity.com/paper/2305.15990/) (2023)
- [A Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning](https://mlsanity.com/paper/2410.08231/) (2024)
- [Foundation Models For Seismic Data Processing: An Extensive Review](https://mlsanity.com/paper/2503.24166/) (2025)
- [Deep Preconditioners and their application to seismic wavefield processing](https://mlsanity.com/paper/2207.09938/) (2022)
- [Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields](https://mlsanity.com/paper/2605.28625/) (2026)

## Abstract (arXiv)

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 most seismic processing workflows. We exploit convolutional neural networks for the joint tasks of interpolation and random noise attenuation of 2D common shot gathers. Inspired by the great contributions achieved in image processing and computer vision, we investigate a particular architecture of convolutional neural network referred to as U-net, which implements a convolutional autoencoder able to describe the complex features of clean and regularly sampled data for reconstructing the corrupted ones. In training phase we exploit part of the data for tailoring the network to the specific tasks of interpolation, denoising and joint denoising/interpolation, while during the system deployment we are able to recover the remaining corrupted shot gathers in a computationally efficient procedure. We consider a plurality of data corruptions in our numerical experiments, including different noise models and different distributions of missing traces. Several examples on synthetic and field data illustrate the appealing features of the aforementioned strategy. Comparative examples show improvements with respect to recently proposed solutions for joint denoising and interpolation.