Suppression of magnetotelluric square wave noise based on a LSTM recurrent neural network
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摘要: 去噪是大地电磁数据处理的重要一环。为了丰富和发展大地电磁时间序列去噪方法,将循环神经网络中的LSTM网络引入大地电磁时间序列方波噪声处理中,将实测无人文干扰的大地电磁时间序列叠加模拟方波噪声作为网络输入,将无噪原始时间序列作为网络的目标输出,训练了1 500次epoch后,网络从仿真含噪信号提取的时间序列与原始时间序列的归一化互相关系数高达0.971 8,说明网络很好地学习了无噪大地电磁时间序列的特征。通过实测含方波噪声信号的去噪试验,表明了本文方法可以有效压制方波噪声干扰,改善阻抗估计质量,为深度学习在大地电磁时间序列处理的应用提供了新思路。Abstract: Denoising is an important part of magnetotelluric data processing. To enrich and develop the denoising method of magnetotelluric time series, this study introduced the LSTM network-one of the recurrent neural networks-into the square wave noise processing of the magnetotelluric time series. Different from previous studies, the measured magnetotelluric time series without human interference superimposed on simulated square wave noise were used as the input of the LSTM network, and the noise-free original time series were used as the target output of the network. After training for 1,500 epochs, the normalized cross-correlation coefficient between the time series extracted from the simulated noise signals by the network and the original time series reached 0.9718, indicating that the network has effectively learned the characteristics of the noise-free magnetotelluric time series. Finally, the denoising test results of measured square wave noise signals show that the proposed method can effectively suppress the interference of square wave noise and improve the estimation quality of impedance. This study provides a new idea for the processing of magnetotelluric time series based on deep learning.
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Key words:
- magnetotellurics /
- time series /
- LSTM /
- deep learning /
- denoising
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