Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network

Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder–decoder neural network is proposed. The method...

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Veröffentlicht in:Applied sciences Jg. 10; H. 18; S. 6621
Hauptverfasser: Zhang, Hang, Ma, Chunchi, Pazzi, Veronica, Zou, Yulin, Casagli, Nicola
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2020
Schlagworte:
convolutional neural network
Datasets
Deep learning
Methods
microseismic monitoring
microseismic signal analysis
Neural networks
Noise
Signal processing
time–frequency domain
Wavelet transforms
China
ISSN:2076-3417, 2076-3417
Online-Zugang:Volltext
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Zusammenfassung:Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder–decoder neural network is proposed. The method simultaneously learns the sparse features in the time–frequency domain, and the mask-related mapping function for signal separation. The results show that the proposed method has an impressive performance on denoising microseismic signals containing various types and intensities of noise. Furthermore, the method works well even when a similar frequency band is shared between the microseismic signals and the noises. The proposed method, compared to the existing methods, significantly improves the signal–noise ratio thanks to minor changes of the microseismic signal (less distortion in the waveform). Additionally, the proposed methods preserve the shape and amplitude characteristics so that it allows better recovery of the real waveform. This method is exceedingly useful for the automatic processing of the microseismic signal. Further, it has excellent potential to be extended to the study of exploration seismology and earthquakes.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10186621