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应用长短期记忆循环神经网络的弱反射信号增强方法 - 中国知网
Source: kns.cnki.net
Article summary:
1. This article proposes a new method for enhancing weak reflection signals in seismic data by using a “raise-weaken-strengthen” approach.
2. The method involves calculating the power reflection coefficients of well logging reflection coefficients, synthesizing seismic records and fitting them to obtain training samples, and then training a long short-term memory (LSTM) recurrent neural network to establish the mapping relationship between the synthetic seismic records and the fitted seismic records.
3. Results from model and actual data applications show that this method can effectively enhance weak reflection signals caused by geological layers, thus improving the reservoir recognition ability of seismic data.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about its proposed method for enhancing weak reflection signals in seismic data. It also provides evidence for its claims through model and actual data applications, which shows that this method can effectively enhance weak reflection signals caused by geological layers, thus improving the reservoir recognition ability of seismic data. Furthermore, it is clear that the authors have conducted extensive research into this topic before writing this article, as evidenced by their references to other related works in the field.
However, there are some potential biases present in the article which could be explored further. For example, while it does mention possible risks associated with its proposed method, such as inaccurate wave extraction leading to false signals being introduced when subtracting strong reflections, it does not provide any further details or explore these risks in depth. Additionally, while it does provide references to other related works in the field, it does not present both sides equally or explore counterarguments to its own claims. Finally, there is no indication of promotional content present in the article; however, given that it was published on China Knowledge Network (CNKI), which is an online academic database owned by Tsinghua University Press Group in Beijing, there may be some bias towards promoting Chinese research and technology within this platform.