Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

TL;DR

This paper introduces a curvelet-based seismic data denoising method that combines a whitening filter and noise variance estimation, effectively handling both coherent and incoherent noise in real and synthetic data.

Contribution

It presents a novel denoising approach that integrates a whitening filter with curvelet transform and noise estimation, improving performance across various noise environments.

Findings

Effective noise reduction in synthetic and real seismic data

Superior performance under both coherent and incoherent noise

Simplified noise estimation process

Abstract

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise).