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

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)

Seismic Signal Denoising Based on Surelet Transform for Energy Exploration

Seismic signals are critical for subsurface energy exploration like oil, coal, and natural gas. Processing these signals while minimizing environmental impacts is crucial but lacking in several appropriate multi-scale geometric analysis (MGA) techniques. This study proposes using the Surelet transform, based on Stein’s unbiased risk estimate (SURE), for seismic denoising. The method combines SURE to find optimal thresholds and linear expansion for coefficient estimation. Experiments on two-dimensional (2D) and three-dimensional (3D) synthetic seismic data showed Surelet achieved higher peak signal-to-noise ratios (PSNR) and faster processing compared to wavelet, curvelet, and wave atom. For example, with 20% noise, Surelet improved PSNR by 6.11% and reduced time by 78.4% versus wave atom. The feasibility of the proposed technique for efficient seismic denoising was demonstrated, highlighting implications for enabling cleaner signals in energy exploration