Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method
Abstract There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26576-2 |
| _version_ | 1797977508794073088 |
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| author | Zhen Zhang Yicheng Ye Binyu Luo Guan Chen Meng Wu |
| author_facet | Zhen Zhang Yicheng Ye Binyu Luo Guan Chen Meng Wu |
| author_sort | Zhen Zhang |
| collection | DOAJ |
| description | Abstract There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor a j is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time. |
| first_indexed | 2024-04-11T05:09:09Z |
| format | Article |
| id | doaj.art-ed8048133f174c55b30b9c7d255e0575 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T05:09:09Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ed8048133f174c55b30b9c7d255e05752022-12-25T12:12:15ZengNature PortfolioScientific Reports2045-23222022-12-0112111610.1038/s41598-022-26576-2Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding methodZhen Zhang0Yicheng Ye1Binyu Luo2Guan Chen3Meng Wu4School of Resources and Environmental Engineering, Wuhan University of Science and TechnologySchool of Resources and Environmental Engineering, Wuhan University of Science and TechnologySchool of Resources and Environmental Engineering, Wuhan University of Science and TechnologySchool of Resources and Environmental Engineering, Wuhan University of Science and TechnologySchool of Resources and Environmental Engineering, Wuhan University of Science and TechnologyAbstract There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor a j is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.https://doi.org/10.1038/s41598-022-26576-2 |
| spellingShingle | Zhen Zhang Yicheng Ye Binyu Luo Guan Chen Meng Wu Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method Scientific Reports |
| title | Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| title_full | Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| title_fullStr | Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| title_full_unstemmed | Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| title_short | Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| title_sort | investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method |
| url | https://doi.org/10.1038/s41598-022-26576-2 |
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