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空间自适应方向全变分地震数据去噪模型 - 中国知网
Source: kns.cnki.net
Article summary:
1. This article proposes a new Space Adaptive Directional Total Variation (SADTV) regularization model for suppressing random noise in seismic data.
2. The model uses a gradient structure tensor to estimate the direction of the same phase axis at each point, and then uses an optimization minimization algorithm to solve the model.
3. Experiments show that this model can improve the vertical resolution of seismic profiles and the lateral continuity of the same phase axis, while also improving signal-to-noise ratio and preserving more geological feature information.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information on its proposed SADTV regularization model for suppressing random noise in seismic data, including how it works, how it is applied to synthetic and real seismic data, and how its results compare with those of other methods. The article also includes references to relevant literature and provides links to related videos.
However, there are some potential biases that should be noted. For example, the article does not explore any counterarguments or alternative approaches to solving this problem; instead, it focuses solely on promoting its own proposed solution. Additionally, there is no discussion of possible risks associated with using this method or any potential drawbacks that should be considered before implementing it in practice. Finally, although the article does provide references to relevant literature, these references are limited in scope and do not provide a comprehensive overview of all existing solutions for this problem.
Topics for further research:
Alternative approaches to suppressing random noise in seismic data
Potential risks associated with SADTV regularization
Drawbacks of SADTV regularization
Comprehensive overview of existing solutions for random noise suppression
Comparison of SADTV regularization with other methods
Practical applications of SADTV regularization