Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data
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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1511243
- Report Number(s):
- LA-UR-18-29323
- Journal Information:
- Seismological Research Letters, Vol. 90, Issue 2A; ISSN 0895-0695
- Publisher:
- Seismological Society of AmericaCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
A dictionary learning algorithm for compression and reconstruction of streaming data in preset order
Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning