Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic
Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic
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基于高阶统计量确定的自适应DnCNN抑制沙漠低频噪声
DOI：
10.1093/gji/ggz363
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发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Baojun Yang
中科院分区：
文献类型：
--
作者：
Yuxing Zhao;Yue Li;Baojun Yang
地球物理学家已经认识到低频地震资料的重要性。然而，低频地震数据的事件恢复和噪声抑制仍有许多障碍需要克服。其中最困难的是如何提高低频信号的信噪比。沙漠地震资料是一种典型的低频地震资料。在沙漠地震资料中，低频噪声（包括表面波和随机噪声）能量强，这在很大程度上降低了沙漠地震资料的信噪比。此外，低频噪声是非平稳和非高斯的。此外，与其他地区的地震资料相比，沙漠地震资料中有效信号与噪声的频谱重叠更为严重。这些都给沙漠地震资料的去噪以及后续的地质构造解释、储层流体预测等勘探工作带来了极大的困难。为了解决这一技术难题，将前馈去噪卷积神经网络（DnCNNs）引入到沙漠地震数据去噪中。dncnn的局部感知和权值共享使其非常适合于信号处理。然而，该网络最初用于抑制噪声图像中的高斯白噪声。为了使DnCNNs适用于沙漠地震数据去噪，对DnCNNs进行了网络参数优化和自适应噪声集构建等综合校正。一方面，通过对去噪参数的优化，选择最适合沙漠地震去噪的网络参数（卷积核、补丁大小和网络深度）；另一方面，在高阶统计量判断的基础上，利用处理后的沙漠地震资料低频噪声构建自适应噪声集，实现自适应自动降噪。不同噪声水平下的合成和实际数据示例证明了自适应dncnn在抑制低频噪声和保留有效信号方面的有效性和鲁棒性。
The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.
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