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使用时频表示和张量分解检测和去噪微地震事件 |IEEE 期刊和杂志 |IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This article introduces a method for detecting and denoising microseismic data using tensor decomposition.
2. The article discusses various existing methods for denoising seismic data, such as spectral filtering, interferometry, STA/LTA, Wiener filtering, wavelet transform-based methods, and rank-reduction modeling.
3. The article also proposes a novel combination of MWF and MWPT which gives better performance and preserves rare signals in the denoising process.
Article analysis:
The article is generally reliable and trustworthy in its presentation of the various existing methods for denoising seismic data. It provides an overview of the different techniques available and their respective advantages and disadvantages. The authors provide evidence to support their claims by citing relevant research papers in the field.
However, there are some potential biases that should be noted. For example, the authors focus mainly on existing methods for denoising seismic data without exploring any potential new or innovative approaches that could be used to improve accuracy or efficiency. Additionally, while the authors do discuss some potential risks associated with certain techniques (e.g., artifacts resulting from spectral filtering), they do not provide a comprehensive overview of all possible risks associated with each technique discussed in the article.
Furthermore, while the authors do present both sides of certain arguments (e.g., discussing both advantages and disadvantages of certain techniques), they do not always present both sides equally or explore counterarguments in depth. Additionally, there is some promotional content in the article as it focuses mainly on existing methods rather than exploring any new or innovative approaches that could be used to improve accuracy or efficiency.
In conclusion, while this article is generally reliable and trustworthy in its presentation of existing methods for denoising seismic data, there are some potential biases that should be noted such as lack of exploration into new or innovative approaches to improve accuracy or efficiency; lack of comprehensive overviews of all possible risks associated with each technique discussed; uneven presentation of both sides of certain arguments; promotional content; and partiality towards existing methods over new ones.