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首页> 外文期刊>Exploration Geophysics >Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principal component analysis
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Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principal component analysis

机译：背景噪声抑制使用鲁棒主成分分析辅助辅助可训练非线性反应扩散

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摘要

Due to the severe interference of background noise, the signal-to-noise ratio of desert seismic data is extremely low. In addition, due to low-frequency characteristics of sand in the Tarim desert region, the background noise in desert seismic data is mainly distributed in low-frequency band, so that the frequency spectrum aliasing of effective signals and background noise is more serious than the general land seismic data. Thus, conventional filtering methods cannot effectively suppress background noise in desert seismic data and recover effective signals. In order to overcome the problem that low-frequency background noise in desert seismic data is hard to suppress, a new method called R-TNRD based on robust principle component analysis (RPCA) algorithm and trainable nonlinear reaction diffusion (TNRD) network is proposed in this paper. By using the good sparsity of RPCA, the input noisy desert seismic data are decomposed into a low-rank matrix and a sparse matrix, and these two matrices contain background noise and effective signals. Due to the serious spectrum aliasing of desert seismic data, conventional thresholds have been unable to extract effective signals from the two matrices obtained by RPCA effectively. Therefore, we introduce TNRD network into desert seismic data denoising. By network training with a low-frequency noise set, the optimisation of TNRD network can be achieved, so as to accurately extract the effective signals from the low-rank matrix and the sparse matrix. In the experimental part, we test the performance of R-TNRD on both synthetic and real seismic data. The results demonstrate that the proposed method can suppress background noise more effectively than conventional methods.
机译：由于背景噪音的严重干扰，沙漠地震数据的信噪比极低。此外，由于塔里木沙漠地区的沙子的低频特性，沙漠地震数据的背景噪声主要分布在低频带中，使有效信号和背景噪声的频谱混叠比一般土地地震数据。因此，传统的滤波方法不能有效地抑制沙漠地震数据中的背景噪声并恢复有效信号。为了克服沙漠地震数据中的低频背景噪声难以抑制的问题，提出了一种基于鲁棒原理分析（RPCA）算法和可培训非线性反应扩散（TNRD）网络的R-TNRD的新方法这篇报告。通过使用RPCA的良好稀疏性，输入嘈杂的沙漠地震数据被分解成低级矩阵和稀疏矩阵，并且这两个矩阵包含背景噪声和有效信号。由于沙漠地震数据的严重频谱叠加，传统的阈值已经无法有效地从RPCA获得的两个矩阵中提取有效信号。因此，我们将TNRD网络引入沙漠地震数据去噪。通过使用低频噪声集的网络训练，可以实现TNRD网络的优化，以便精确提取来自低秩矩阵和稀疏矩阵的有效信号。在实验部分中，我们在合成和实际地震数据上测试R-TNRD的性能。结果表明，所提出的方法可以比传统方法更有效地抑制背景噪声。

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