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Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 650 - 665 Titre : Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network Type de document : Article/Communication Auteurs : Yuxing Zhao, Auteur ; Yue Li, Auteur ; Baojun Yang, Auteur Année de publication : 2020 Article en page(s) : pp 650 - 665 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] algorithme de filtrage [Termes IGN] analyse multiéchelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] désert [Termes IGN] enregistrement de données [Termes IGN] filtrage du bruit [Termes IGN] filtre passe-bande [Termes IGN] interruption du signal [Termes IGN] lutte contre le bruit [Termes IGN] rapport signal sur bruit [Termes IGN] reconstruction du signal [Termes IGN] séisme Résumé : (auteur) Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method. Numéro de notice : A2020-076 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2938836 Date de publication en ligne : 24/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2938836 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94608 [article]