Data augmentation and its application in distributed acoustic sensing data denoising
Data augmentation and its application in distributed acoustic sensing data denoising
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DOI：
10.1093/gji/ggab345
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发表时间：
2021-08
影响因子：
2.8
通讯作者：
Yuxing Zhao;Yapeng Li;N. Wu
中科院分区：
文献类型：
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作者：
Yuxing Zhao;Yapeng Li;N. Wu
作为一种数据驱动的方法，深度学习模型的性能在很大程度上取决于训练数据集的数量和质量，这极大地限制了深度学习在小数据集任务中的应用。遗憾的是，有时我们需要利用有限的小数据集来完成任务，比如分布式光纤声波传感（DAS）数据去噪。然而，使用小数据集训练网络可能会导致过拟合，进而造成网络泛化能力不佳。为解决这一问题，我们提出一种基于生成对抗网络与深度卷积神经网络相结合的方法。首先，我们利用一个小的噪声数据集训练生成对抗网络，以生成合成噪声样本，然后使用这些合成噪声样本扩充噪声数据集。接下来，我们利用扩充后的噪声数据集以及通过正演模拟获得的信号数据集构建一个合成训练集。最后，在构建好的合成训练集上训练一个基于卷积神经网络的去噪网络。实验结果表明，扩充后的数据集能够有效提升网络的去噪性能和泛化能力，并且在扩充数据集上训练的去噪网络能够更有效地降低DAS数据中的各类噪声。
As a data-driven approach, the performance of deep learning models depends largely on the quantity and quality of the training data sets, which greatly limits the application of deep learning to tasks with small data sets. Unfortunately, sometimes we need to use limited small data sets to complete our tasks, such as distributed acoustic sensing (DAS) data denoising. However, using a small data set to train the network may cause overfitting, resulting in poor network generalization. To solve this problem, we propose an approach based on the combination of a generative adversarial network and a deep convolutional neural network. First, we used a small noise data set to train a generative adversarial network to generate synthetic noise samples, and then used these synthetic noise samples to augment the noise data set. Next, we used the augmented noise data set and the signal data set obtained through forward modelling to construct a synthetic training set. Finally, a denoising network based on a convolutional neural network was trained on the constructed synthetic training set. Experimental results show that the augmented data set can effectively improve the denoising performance and generalization ability of the network, and the denoising network trained on the augmented data set can more effectively reduce various kinds of noise in the DAS data.