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Title: Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data

Abstract

Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. Here, we illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit for feature extraction can be used for signal-to-noise ratio (SNR) enhancement. In this strategy, waveform characteristics are directly learned from provided data samples; the created dictionary is then used for signal extraction. This combination prevents the need to set a predefined dictionary, and it becomes computationally efficient because learning is only done on smaller data portions. Because the dictionary is learned from data, there is no assumption regarding wavefront shape or form. Tests on synthetic and field data demonstrate the better denoising performance in terms of SNR enhancement compared to other methods.

Authors:
 [1];  [1]; ORCiD logo [2]
  1. Univ. of Alberta, Edmonton, AB (Canada). Dept. of Physics
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1511243
Report Number(s):
LA-UR-18-29323
Journal ID: ISSN 0895-0695
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Seismological Research Letters
Additional Journal Information:
Journal Volume: 90; Journal Issue: 2A; Journal ID: ISSN 0895-0695
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Zhang, Chao, van der Baan, Mirko, and Chen, Ting. Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data. United States: N. p., 2018. Web. doi:10.1785/0220180302.
Zhang, Chao, van der Baan, Mirko, & Chen, Ting. Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data. United States. https://doi.org/10.1785/0220180302
Zhang, Chao, van der Baan, Mirko, and Chen, Ting. Wed . "Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data". United States. https://doi.org/10.1785/0220180302. https://www.osti.gov/servlets/purl/1511243.
@article{osti_1511243,
title = {Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data},
author = {Zhang, Chao and van der Baan, Mirko and Chen, Ting},
abstractNote = {Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. Here, we illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit for feature extraction can be used for signal-to-noise ratio (SNR) enhancement. In this strategy, waveform characteristics are directly learned from provided data samples; the created dictionary is then used for signal extraction. This combination prevents the need to set a predefined dictionary, and it becomes computationally efficient because learning is only done on smaller data portions. Because the dictionary is learned from data, there is no assumption regarding wavefront shape or form. Tests on synthetic and field data demonstrate the better denoising performance in terms of SNR enhancement compared to other methods.},
doi = {10.1785/0220180302},
journal = {Seismological Research Letters},
number = 2A,
volume = 90,
place = {United States},
year = {Wed Dec 05 00:00:00 EST 2018},
month = {Wed Dec 05 00:00:00 EST 2018}
}

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