Interpolation and Denoising of Seismic Data using Convolutional Neural Networks

TL;DR

This paper presents a convolutional neural network approach, specifically using a U-net architecture, to jointly interpolate and denoise 2D seismic data, improving data quality for seismic processing workflows.

Contribution

It introduces a U-net based neural network model for simultaneous seismic data interpolation and denoising, demonstrating effectiveness on synthetic and field data with various corruptions.

Findings

Improved reconstruction of corrupted seismic data

Effective handling of different noise models and missing data

Outperforms recent methods in joint denoising and interpolation

Abstract

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.