Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short‐time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal‐to‐noise ratios (SNRs) by ∼5 dB⁠, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ∼0.80⁠) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ∼2–5 improvement in SNR over band‐pass filtering and can suppress many types of noise that band‐pass filtering cannot. For individual waveforms, the improvement can be as high as ∼15 dB⁠.

中文翻译：

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深度学习去噪技术在犹他州的区域距离地震数据中的应用

地震波形数据通常受到来自各种来源的噪声的污染。有效地抑制这种噪声，以便可以成功地利用剩余的感兴趣信号，仍然是地震学界的一个基本问题。迄今为止，最常见的噪声抑制方法已经基于频率滤波。但是，当感兴趣的信号和噪声共享相似的频带时，这些方法效果较差。受音乐信息检索领域的源分离研究（Jansson等人，2017）和地震学的最新研究（Zhu等人，2019）的启发，我们实现了一种地震降噪方法，该方法使用了经过训练的深度卷积神经网络（ CNN）模型将输入波形分解为感兴趣的信号和噪声。在我们的方法中，CNN为输入信号提供信号屏蔽和噪声屏蔽。估计信号的短时傅立叶变换（STFT）是通过将信号掩码与输入信号的STFT相乘而获得的。为了构建和测试降噪器，我们使用了由犹他大学地震台站网络记录的经过仔细编译的地震图信号和噪声数据集。涉及9000多个构建波形的测试结果表明，平均而言，降噪器可将信噪比（SNR）提高约5dB⁠，并且大多数恢复的信号波形与目标波形具有高度相似性（平均相关系数约为0.80？），并且失真很小。在真实数据上的应用表明，我们的降噪器在带通滤波方面的SNR平均提高了约2-5倍，并且可以抑制带通滤波无法实现的许多类型的噪声。对于单个波形，改善幅度可能高达15dB⁠。