Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network
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摘要: 为解决浅地层剖面数据噪声多、分辨率低问题，本文将环形生成对抗网络的方法应用于浅地层剖面资料的去噪，实现智能去噪。首先，选择具有特殊对称生成对抗网络循环机制以及“循环一致性”损失的环形生成对抗性网络，并对其进行结构改进，提升网络学习和训练的性能。然后，基于优化的浅地层剖面样本集训练网络，实现对于浅地层剖面数据随机噪声的去除，提升数据的信噪比。通过对实验和实际资料的试算，以及与传统带通滤波方法的对比，验证本文方法对浅地层剖面数据去噪的有效性和适应性。Abstract: This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data. To do this, the cycle generative adversarial network with special symmetric generation countermeasure network cycle mechanism and "cycle consistency loss" was selected. We improved the performance of the network learning and training by optimizing the network structure. Next, based on the optimized shallow profile sample set training network, random noise was removed from the shallow profile data and the signal-to-noise ratio of the data was improved. The effectiveness and adaptability of this method for denoising shallow profile data were verified by trial calculations of experimental and actual data and by comparison with the traditional band-pass filtering method.
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Key words:
- artificial intelligence /
- shallow profile data /
- random noise /
- data denoising
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表 1 两种去噪方法PSNR对比
Table 1. PSNR comparison of two denoising methods
去噪方法 PSNR/dB 带通滤波去噪 18.729 CycleGAN去噪 25.247 
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