Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the following steps: Firstly, we randomly mute a part of the input data of the neural network according to a certain percentage, and then the network outputs the reconstructed data influenced by this randomly mute. The objective function measures the distance between the input data and the reconstructed data. Secondly, we use the adaptive moment estimation algorithm to minimize the distance, and the network adjusts its internal parameters so that sparse representations can be captured by the multiple processing layers of the neural network. Finally, we take the same proportion of random mute on the raw seismic data which are fed to the trained neural network. Through this network, reconstruction of seismic data and attenuation of random noise are completed simultaneously. We use both synthetic and field data to testify the feasibility and applicability of the proposed method. Synthetic data experiment indicates that the proposed method achieves better denoised results than the conventional methods. Field data applications further demonstrate its superiority and practicality.

中文翻译：

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通过信号重建来衰减地震噪声：一种无监督的机器学习方法

随机噪声衰减是地震数据处理中改善地震数据质量和信噪比必不可少的步骤。我们采用无监督的机器学习方法，通过信号重建策略来衰减随机噪声。该方法可以通过以下步骤实现：首先，我们根据一定百分比将神经网络的部分输入数据随机静音，然后网络输出受此随机静音影响的重构数据。目标函数测量输入数据和重建数据之间的距离。其次，我们使用自适应矩估计算法来最小化距离，并且网络调整其内部参数，以便稀疏表示可以被神经网络的多个处理层捕获。最后，我们在原始地震数据上采用相同比例的随机静音，然后将其输入训练后的神经网络。通过该网络，可以同时完成地震数据的重建和随机噪声的衰减。我们使用综合数据和现场数据来证明该方法的可行性和适用性。综合数据实验表明，与常规方法相比，该方法具有更好的去噪效果。现场数据的应用进一步证明了其优越性和实用性。综合数据实验表明，与常规方法相比，该方法具有更好的去噪效果。现场数据的应用进一步证明了其优越性和实用性。综合数据实验表明，与常规方法相比，该方法具有更好的去噪效果。现场数据的应用进一步证明了其优越性和实用性。