Research Repository

Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning

Nazari Siahsar, Mohammad Amir and Gholtashi, Saman and Abolghasemi, Vahid and Chen, Yangkang (2017) 'Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning.' Signal Processing, 141. pp. 309-321. ISSN 0165-1684

Full text not available from this repository. (Request a copy)


As a major concern, the existence of unwanted energy and missing traces in seismic data acquisition can degrade interpretation of such data after processing. Instead of analytical dictionaries, data-driven dictionary learning (DDL) methods as a flexible framework for sparse representation, are dedicated to the problem of denoising and interpolation. Due to their meaningful geometric repetitive structures, seismic data are intrinsically low-rank in the time-space domain. On the other hand, noise and missing traces increase the rank of the noisy data. Therefore, the clean data, unlike noise and missing traces, can be modeled as a linear combination of a few elements from a learned dictionary. In this paper, a parts-based 2D DDL scheme is introduced and evaluated for simultaneous denoising and interpolation of seismic data. A special case of versatile non-negative matrix factorization (VNMF) is used to learn a dictionary. In VNMF, smoothness constraint can improve interpolation, and sparse coding helps improving denoising. The proposed method is tested on synthetic and real-field seismic data for simultaneous denoising and interpolation. Through experimental results, the proposed method is determined to be an effective and robust tool that preserves significant components of the signal. Comparison with four state-of-the-art methods further verifies its superior performance.

Item Type: Article
Uncontrolled Keywords: 2D seismic data; Denoising; Interpolation; Sparse coding; Dictionary learning; Versatile non-negative matrix factorization
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Apr 2020 13:56
Last Modified: 15 Jan 2022 01:28

Actions (login required)

View Item View Item