Abstract
Machine learning methods face two main challenges in denoising tasks. One is the lack of supervised training data, and the other is the limited knowledge of complex unknown noise. In this paper, for seismic denoising, we propose a new method with three techniques to handle them effectively. First, a Generative Adversarial Network (GAN) is employed to generate a large number of paired clean-noisy data using real noise. Second, a deep denoising autoencoder (DDAE) is pre-trained using these data. Third, a transfer learning technique is used to train the DDAE further on a few field data. We have assessed the proposed method based on qualitative and quantitative analysis. Results show that the method can suppress seismic data noise well.
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Min, F., Wang, LR., Pan, SL., Song, GJ. (2021). DDAE-GAN: Seismic Data Denoising by Integrating Autoencoder and Generative Adversarial Network. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_4
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DOI: https://doi.org/10.1007/978-3-030-87334-9_4
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