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基于Noise-to-Noise自监督学习的地震数据去噪方法 - 中国知网
Source: kns.cnki.net
Article summary:
1. Random noise is an unavoidable part of seismic data collected during exploration, and high signal-to-noise ratio data is the basis for seismic inversion and interpretation.
2. Recent developments in deep learning have enabled convolutional neural networks to be used for seismic data denoising with great success.
3. This article proposes a Noise-to-Noise self-supervised learning method for seismic data denoising, which only requires noisy seismic data to train the denoising network, and tests on both synthetic and real data show that it can effectively remove random noise from seismic data with better performance than supervised learning methods limited by the construction of datasets and traditional denoising methods.
Article analysis:
The article is generally trustworthy and reliable, as it provides a detailed description of the proposed Noise-to-Noise self-supervised learning method for seismic data denoising, along with evidence from both synthetic and real data tests that demonstrate its effectiveness in removing random noise from seismic data. The article does not appear to contain any biases or one-sided reporting, as it presents both sides of the argument equally. Furthermore, all claims made are supported by evidence provided in the form of test results on both synthetic and real data. There are no missing points of consideration or missing evidence for any claims made, nor are there any unexplored counterarguments or promotional content present in the article. The article also notes possible risks associated with using this method, such as overfitting due to insufficient training samples or incorrect labels generated by noisy input signals. Therefore, overall this article can be considered trustworthy and reliable.