Corpus ID: 204907055

Attenuating Random Noise in Seismic Data by a Deep Learning Approach

  title={Attenuating Random Noise in Seismic Data by a Deep Learning Approach},
  author={Xing Zhao and Ping Lu and Yanyan Zhang and Jianxiong Chen and Xiaoyang Rebecca Li},
In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing. Here, we propose a model to leverage the deep-learning model for this task. Rather than directly applying an existing de-noising model from ordinary images to the seismic data, we have designed a particular deep-learning model, based on residual neural networks. It is named as N2N-Seismic, which has a strong ability to recover the seismic… Expand


Random noise attenuation by f-x empirical mode decomposition predictive filtering
  • Yangkang Chen, Jitao Ma
  • Computer Science, Geology
  • 2014
A novel denoising method termed f‐x empirical-mode decomposition (EMD) predictive filtering is developed, which solved the problem that makes f-x EMD ineffective with complex seismic data and removed the limitation of conventional f‐ x predictive filtering when dealing with multidip seismic profiles. Expand
Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis
We present a rank reduction algorithm that permits simultaneous reconstruction and random noise attenuation of seismic records. We based our technique on multichannel singular spectrum analysisExpand
Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation
A new noise suppression algorithm for seismic data denoising that is visually and quantitatively superior to the other well-established noise reduction methods is described. Expand
Local singular value decomposition for signal enhancement of seismic data
Singular value decompositionSVD is a coherency-based technique that provides both signal enhancement and noise suppression. It has been implemented in a variety of seismic applications — mostly on aExpand
Natural Image Denoising with Convolutional Networks
An approach to low-level vision is presented that combines the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models to avoid computational difficulties in MRF approaches that arise from probabilistic learning and inference. Expand
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising, and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance. Expand
Multicomponent f-x seismic random noise attenuation via vector autoregressive operators
We propose an extension of the traditional frequency-space (f-x) random noise attenuation method to three-component seismic records. For this purpose, we develop a three-component vectorExpand
Toward Convolutional Blind Denoising of Real Photographs
A convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs and a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Expand
Seismic data interpolation and denoising in the frequency-wavenumber domain
I introduce a unified approach for denoising and interpolation of seismic data in the frequency-wavenumber (f-k) domain. First, an angular search in the f-k domain is carried out to identify a sparseExpand
Improved random noise attenuation using f−x empirical mode decomposition and local similarity
Conventional f−x empirical mode decomposition (EMD) is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events. However, when a seismic event isExpand