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Research on microseismic denoising method based on CBDNet

Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose a new denoising method based on convolutional blind denoising network (CBDNet). The method is partially modified for image denoising network CBDNet to make it suitable for one–dimensional data denoising. At present, most of the existing filtering methods are proposed for the Gaussian white noise denoising. In contrast, the proposed method also learns the wind noise, construction noise, traffic noise and mixed noise through the strategy of residual learning. The full convolution subnetwork is used to estimate the noise level, which significantly improves the signal-to-noise ratio and its performance of removing the correlated noise. The model is trained with different types of real noise and random noise. The denoising result is evaluated by corresponding indexes and compared with other denoising methods. The results show that the proposed method has better denoising performance than traditional methods, and it has a superior noise suppression level for oil well construction noise and mixed noise. The proposed method can suppress the interference of time–frequency overlapped end to end and still have noise suppression and event detection capability even if the signal is superimposed on other types of noise.

Ketersediaan
307551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
11 hlm PDF, 4.803 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.4, December 2023
Subjek
Deep learning
Denoising
Microseismic
Surface monitoring
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Research on microseismic denoising method based on CBDNet
    Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose a new denoising method based on convolutional blind denoising network (CBDNet). The method is partially modified for image denoising network CBDNet to make it suitable for one–dimensional data denoising. At present, most of the existing filtering methods are proposed for the Gaussian white noise denoising. In contrast, the proposed method also learns the wind noise, construction noise, traffic noise and mixed noise through the strategy of residual learning. The full convolution subnetwork is used to estimate the noise level, which significantly improves the signal-to-noise ratio and its performance of removing the correlated noise. The model is trained with different types of real noise and random noise. The denoising result is evaluated by corresponding indexes and compared with other denoising methods. The results show that the proposed method has better denoising performance than traditional methods, and it has a superior noise suppression level for oil well construction noise and mixed noise. The proposed method can suppress the interference of time–frequency overlapped end to end and still have noise suppression and event detection capability even if the signal is superimposed on other types of noise.
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