Motion Sensor Noise Attenuation Using Deep Learning

Bagher Farmani; Yash Pal; Morten W. Pedersen; Edwin Hodges

doi:10.3997/1365-2397.fb2023010

ISSN: 0263-5046
E-ISSN: 1365-2397

Motion Sensor Noise Attenuation Using Deep Learning
Authors Bagher Farmani¹, Yash Pal¹, Morten W. Pedersen¹, Edwin Hodges¹
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Affiliations: ¹ PGS
Source: First Break, Volume 41, Issue 2, Feb 2023, p. 45 - 51
DOI: https://doi.org/10.3997/1365-2397.fb2023010
- Published online: 01 Feb 2023

Abstract

A method is proposed to attenuate both instrumental and environmental noise from motion sensor records in multisensor streamer acquisition. The main elements are two convolutional neural network models. The first model attenuates vertical narrow band high amplitude noise mainly generated by the instruments attached to the streamers. The second model attenuates widespread background noise mainly associated with environmental conditions. To reduce the risk of possible signal loss an addback flow in the curvelet domain is used. The motivation for the work presented here was to develop a fully automated noise attenuation method that eliminates the need for time-consuming and subjective user parameter testing. The method has been validated using seismic data from different parts of the world and shown to consistently produce superior results to other state-of-the-art noise attenuation processes.

EAGE Publications BV

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2024-04-29

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References

Anwar, S. and Barnes, N. [2019]. Real Image Denoising with Feature Attention.IEEE International Conference on Computer Vision (ICCV).
[Google Scholar]
Bekara, M. and van der Baan, M. , [2010]. High-amplitude noise detection by the expectation-maximization algorithm with application to swell-noise attenuation.Geophysics, 75(3), pp.V39–V49.
[Google Scholar]
Bekara, M. and A.Day . [2019]. Automatic QC of denoise processing using a machine learning classification.First Break, 37(9), 51–58.
[Google Scholar]
Chen, K. and Sacchi, M.D. [2017]. Robust f‐x projection filtering for simultaneous random and erratic seismic noise attenuation.Geophysical Prospecting, 65(3), pp.650–668.
[Google Scholar]
Day, A., Klüver, T., Söllner, W., Tabti, H. and Carlson, D. [2013]. Wavefield-separation methods for dual-sensor towed-streamer data.Geophysics, 78(2), pp.WA55–WA70.
[Google Scholar]
Farmani, B. and Pedersen, M.W. [2020a]. Application of a convolutional neural network to classification of swell noise attenuation. SEG Technical Program Expanded Abstracts: 2868–2872.
[Google Scholar]
Farmani, B. and Pedersen, M.W. [2020b]. Application of Convolutional Neural Network in Automated Swell Noise Attenuation.EAGE 2020 Annual Conference & Exhibition Online, Dec 2020, Volume 2020, p.1–5.
[Google Scholar]
Farmani, B. and Pedersen, M.W. [2022]. Stepping Towards Automated Multisensor Noise Attenuation Guided by Deep Learning.83rd EAGE Annual Conference & Exhibition, Jun 2022, Volume 2022, p.1–5.
[Google Scholar]
Kumar, A., Dancer, K., Rayment, T., Hampson, G. and Burgess, T. [2022]. Deep Learning Swell Noise Estimation.First Break, 40(9), 31–36.
[Google Scholar]
Nguyen, T. and Liu, Y.J. [2017]. Seismic Noise Attenuation Using Curve-let Transform and Dip Map Data Structure.79th EAGE Conference & Exhibition 2017, Jun 2017, Volume 2017, p.1–5.
[Google Scholar]
Ronneberger, O., Fischer, P. and Brox, T. [2015]. U-Net: Convolutional Networks for Biomedical Image Segmentation.Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9351: 234–241.
[Google Scholar]
Valenciano, A., Brusova, O. and Cheng, C. [2022]. Efficient Swell Noise Removal Using a Global Deep Neural Network Model.First Break, 40(2), 51–55.
[Google Scholar]
Walpole, J., Hallett, K., Brown, E. and Brittan, J. [2020]. Visual Identification of Noisy Seismic Records with Machine Learning.EAGE 2020 Annual Conference & Exhibition Online, Dec 2020, Volume 2020, p.1–5.
[Google Scholar]

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Motion Sensor Noise Attenuation Using Deep Learning

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Motion Sensor Noise Attenuation Using Deep Learning

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