Extension usage examples:
Here's how our browser extension sees the article:
基于采样策略的自监督学习在地震去噪中的应用 - 中国知网
Source: kns.cnki.net
Article summary:
1. Supervised deep learning methods have been used in the past few years to suppress seismic noise, but they require a large amount of noise data and clean seismic data labels.
2. Inspired by computer vision ideas, this article proposes using self-supervised learning methods to train deep neural networks without requiring clean seismic data labels.
3. The proposed method was tested on two seismic datasets with random noise and was able to accurately separate the seismic signal from the random noise, but not from coherent noise.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for its claims and presents both sides of the argument equally. It is well-researched and provides detailed information about the proposed method and its results. The authors also provide references to other relevant research papers which adds credibility to their work.
However, there are some potential biases that should be noted. For example, the authors do not discuss any possible risks associated with using self-supervised learning methods for seismic noise suppression or any potential limitations of their proposed method. Additionally, while they provide references to other relevant research papers, they do not explore any counterarguments or alternative approaches that could be used for this task.
In conclusion, while this article is generally reliable and trustworthy, there are some potential biases that should be noted when evaluating its content.