Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method
Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method
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DOI：
10.1109/lgrs.2020.3023706
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发表时间：
2022
影响因子：
4.8
通讯作者：
Xintong Dong;Yue Li;T. Zhong;N. Wu;Hongzhou Wang
中科院分区：
文献类型：
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作者：
Xintong Dong;Yue Li;T. Zhong;N. Wu;Hongzhou Wang
分布式光纤声波传感（DAS）是地震勘探领域一项新兴且蓬勃发展的技术。由于其具有较强的耐高温高压性能、高灵敏度、高精度（道间距可达约1米的精度）等特点，DAS技术已逐渐应用于垂直地震剖面（VSP）勘探。然而，实际的DAS - VSP数据总是受到随机噪声和相干噪声的干扰，这极大地影响了DAS - VSP数据的质量。为了压制背景噪声并提高信噪比（SNR），本文提出了一种基于泄漏整流线性单元（Leaky ReLU）和正演模拟的卷积神经网络（CNN），命名为L - FM - CNN。在网络架构方面，采用Leaky ReLU作为CNN的激活函数，这能够增强训练后的CNN去噪模型对微弱有效信号的恢复能力。对于训练数据集，通过正演模型的复杂性和物理参数的多样化，为DAS - VSP数据构建了一个高逼真度的理论纯地震数据集。此外，提出了一种结合能量比矩阵（ERM）的新型均方误差（MSE）损失函数。ERM可以在网络训练过程中调整信号块与噪声块之间的信噪比，从而提高训练后的CNN去噪模型对不同信噪比的DAS - VSP数据，尤其是极低信噪比的DAS - VSP数据的鲁棒性。合成数据实验和实际数据实验均证明了所提L - FM - CNN的有效性。
Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.