基于生成对抗网络的半航空瞬变电磁噪声数据扩充方法

doi:10.11720/wtyht.2024.1211

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Abstract

The semi-airborne transient electromagnetic (SATEM) noise data, exhibiting intricate forms, high acquisition costs, and small volumes, cannot be augmented using conventional augmentation methods, thus significantly hindering the subsequent denoising work. Hence, this study proposed a data augmentation method for SATEM signals based on the generative adversarial network (GAN). By designing the generator as a long short-term memory (LSTM) network and training the generator and discriminator models based on the dataset of real-measured SATEM noise, this study obtained a generator model that can generate simulated noise data. Then, this study analyzed the distributions of the simulated noise generated by the generator and the real-measured noise. Moreover, this study compared the performance of the denoising network before and after augmentation, demonstrating the effectiveness of this method for augmenting real-measured SATEM noise data.

Key words： semi-airborne transient electromagnetics generative adversarial network real-measured noise data augmentation

Received: 22 May 2023 Published: 27 June 2024

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	Articles by authors
	Wei FENG
	Hao FENG
	Li-Jiang XIAO
	Pin-Ming CHEN
	Dong LIU
	Yong-Xin WANG
	Xiao-Sheng ZHOU
	Huai-Feng SUN
	Zhen WANG

Cite this article:

Wei FENG,Hao FENG,Li-Jiang XIAO, et al. A data augmentation method for semi-airborne transient electromagnetic noise based on a generative adversarial network[J]. Geophysical and Geochemical Exploration, 2024, 48(3): 812-819.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2024.1211 OR https://www.wutanyuhuatan.com/EN/Y2024/V48/I3/812

The schematic of GAN structure

The schematic diagram of LSTM unit structure

The schematic of generator G structure

Single sample point noise data

Single cycle noise data

Experimental Environment

Real noise data and simulate noise data form

Gaussian distribution plot of real noise data and simulated noise data

Distribution chart of signal-to-noise ratio and correlation coefficient before and after denoising of synthetic data with noise
a—signal-to-noise ratio and correlation coefficient of data before noise reduction; b—signal-to-noise ratio and correlation coefficient of data after noise reduction (without using augmented data); c—signal-to-noise ratio and correlation coefficient of data after noise reduction (using augmented data); d—the difference in denoising results between using and without using augmented data

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