• 应用地球物理学Ⅰ（油气及金属矿产地球物理勘探） •

### 基于深度卷积生成对抗网络的瑞雷波信号随机噪声去除

1. 1.中国石化华东分公司石油勘探开发研究院, 南京 210007
2.中国石化石油物探技术研究院,南京 211103
• 收稿日期:2020-03-06 修回日期:2020-08-09 出版日期:2020-12-20 发布日期:2020-12-26
• 作者简介:俞若水,女,1993年生,硕士研究生,主要从事地震资料解释方法研究. E-mail: 1041635376@qq.com
• 基金资助:
国家科技重大专项(2016ZX05061);中国石化科技部项目(P19017-3)

### Deep convolutional generative adversarial network for random noise attenuation in Rayleigh wave signal

YU Ruo-shui1(), ZHANG Yong1, ZHOU Chuang2

1. 1. Research Institute of Petroleum Exploration and Development, East China Company Sinopec, Nanjing 210007, China
2. Sinopec Geophysical Research Institute, Nanjing 211103, China
• Received:2020-03-06 Revised:2020-08-09 Online:2020-12-20 Published:2020-12-26

Abstract:

Rayleigh wave exploration is an emerging method of environmental and engineering geophysical exploration. Rayleigh wave data collected in the field often contains various noise. In order to denoise the Rayleigh wave signal, we introduce the deep convolution generating adversarial network in the field of deep learning into the Rayleigh wave exploration field in engineering exploration as a new method of Rayleigh wave signal denoising. We develop a Rayleigh wave signal denoising method based on the Deep Convolutional Generative Adversarial Network (DCGAN). The key requirement of the algorithm is to construct a DCGAN that is suitable for random noise attenuation in Rayleigh wave signal, which includes a generator and a discriminator. The discriminator is composed by a Convolutional Neural Network (CNN), which is used to aid the training of the generator. The generator composed by a Fully Convolutional Network (FCN), which is designed to learn a mapping from noisy data to non-noisy data. The application of the DCGAN includes three steps: data preprocessing, training, and noise attenuation. Convolutional layers are added with activation functions, using batch normalization optimization algorithms, and discarding the regularization layer to prevent overfitting, so that DCGAN training can guarantee higher resolution and accuracy, keeping more details of the original data makes the training process more stable. Once the network training is completed, the denoising process does not require more manual adjustment of parameters, reducing labor costs. The denoising test was performed on the actual data, for Rayleigh wave data with different amplitude noise, the methods in this paper have certain feasibility, and have achieved good results for Rayleigh wave signal denoising. The denoising effect was evaluated from the two aspects of seismic data and dispersion curve. Comparing with the common methods such as wavelet transform method and F-X deconvolution method, DCGAN based method shows better accuracy in the case of different proportions of noise, which has certain reference significance for the choice of Rayleigh wave signal denoising method.