| 基于Seislet-TV双正则化约束的地震随机噪声压制方法 |
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| 引用本文： | 张鹏,刘洋,刘鑫明,刘财,张亮.基于Seislet-TV双正则化约束的地震随机噪声压制方法[J].地球物理学报,2020,63(5):2056-2068. |
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| 作者姓名： | 张鹏 刘洋 刘鑫明 刘财 张亮 |
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| 作者单位： | 1. 华北理工大学矿业工程学院, 河北唐山 063210;2. 吉林大学地球探测科学与技术学院, 长春 130026;3. 中国石油吉林油田公司乾安采油厂, 吉林松原 138000 |
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| 基金项目： | 国家自然科学基金项目（41522404，41774127）资助. |
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| 摘 要： | 人工地震数据总是受到随机噪声的干扰,地震数据时-空变的特性使得常规去噪方法处理效果并不理想,容易导致有效信号的损失.目前广泛应用的预测滤波类方法存在处理时变数据能力不足的问题.随着压缩感知理论的不断完善,稀疏变换阈值算法能够解决时变地震数据噪声压制问题,但是常规的稀疏变换方法,如傅里叶变换,小波变换等,并不是特殊针对地震数据设计的,很难提供地震数据最佳的压缩特征,同时,常规阈值算法容易导致去噪结果过于平滑.因此开发更加有效的时-空变地震数据信噪分离方法具有重要的工业价值.本文将地震数据信噪分离问题归纳为数学基追踪问题,在压缩感知理论框架下,利用特殊针对地震数据设计的VD-seislet稀疏变换方法,结合全变差(TV)算法,构建seislet-TV双正则化条件,并利用分裂Bregman迭代算法求解约束最优化问题,实现地震数据的有效信噪分离.通过理论模型和实际数据测试本文方法,并且与工业标准FXdecon方法进行比较,结果表明基于seislet-TV双正则化约束条件的迭代方法能够更加有效地保护时-空变地震信号,压制地震数据中的强随机噪声.
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| 关 键 词： | 地震数据信噪分离 压缩感知 全变差 seislet-TV双正则化 分裂Bregman迭代 |
| 收稿时间： | 2018-11-06 |
Suppressing seismic random noise based on Seislet-TV dual regularization |
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| ZHANG Peng,LIU Yang,LIU XinMing,LIU Cai,ZHANG Liang.Suppressing seismic random noise based on Seislet-TV dual regularization[J].Chinese Journal of Geophysics,2020,63(5):2056-2068. |
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| Authors: | ZHANG Peng LIU Yang LIU XinMing LIU Cai ZHANG Liang |
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| Institution: | 1. College of Mining Engineering, North China University of Science and Technology, Tangshan Hebei 063210, China;2. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;3. Qian An Oil Factory, Jilin Oilfield, CNPC, Songyuan Jilin 138000, China |
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| Abstract: | Artificial seismic data are always disturbed by random noise. In conventional denoising methods, the temporal-spatial variation of seismic data makes results unsatisfactory and leads to loss of effective signal. At present, the widely used predictive filtering methods have shown their disadvantages when dealing with time-varying data. With the continuous improvement of compressed sensing theory, sparse transforms which combine with threshold methods show the ability to suppress random noise of time-varying seismic data. But the traditional sparse transforms like Fourier transform, wavelet and so on, which were not specially designed for seismic data. So it is difficult for them to provide the best compression characteristics for seismic data. At the same time, the traditional threshold methods can easily lead to over smooth denoising results. Therefore, it has great industrial value to develop a more effective time-space-varying seismic signal and noise separation method. In this paper, we treat seismic data denoising as basic pursuit problem. Based on the frame of compressed sensing (CS), we combine VD-seislet transform which is specially designed for seismic data with total variation to construct seislet-TV dual regularization. And a split Bregman iteration algorithm is used to solve the constrained minimization problem in order to separate signal and noise effectively. According to the Tests on synthetic and field data test and comparison with the industry standard FXdecon method show that the denoising results of the proposed iteration method based on seislet-TV dual regularization can protect time-space-varying seismic data and suppress the strong random noise in seismic data more effectively. |
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| Keywords: | Seismic signal and noise separation Compressed sensing Total variation Seislet-TV dual regularization Split Bregman iteration |
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