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Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method
GAO Han
University of Science and Technology of China
摘要:
Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio (SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively. We demonstrate this method with synthetic and field data.
关键词:  Deep learning  Convolutional neural network  Denoising  Data interpolation  Iterative alternating
DOI:DOI: 10.19743/j.cnki.0891-4176.201901003
分类号:
基金项目:National Natural Science Foundation of China (Grant No. 41674120)
Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method
GAO Han
University of Science and Technology of China
Abstract:
Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio (SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively. We demonstrate this method with synthetic and field data.
Key words:  Deep learning  Convolutional neural network  Denoising  Data interpolation  Iterative alternating