A Nonparametric Method for Automatic Denoising of Microseismic Data
Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step A...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/4367201 |
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| _version_ | 1832565650997903360 |
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| author | Pingan Peng Liguan Wang |
| author_facet | Pingan Peng Liguan Wang |
| author_sort | Pingan Peng |
| collection | DOAJ |
| description | Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data. |
| format | Article |
| id | doaj-art-6904124ab31d4c31b80142481c055ed5 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6904124ab31d4c31b80142481c055ed52025-02-03T01:07:00ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/43672014367201A Nonparametric Method for Automatic Denoising of Microseismic DataPingan Peng0Liguan Wang1School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaNoise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.http://dx.doi.org/10.1155/2018/4367201 |
| spellingShingle | Pingan Peng Liguan Wang A Nonparametric Method for Automatic Denoising of Microseismic Data Shock and Vibration |
| title | A Nonparametric Method for Automatic Denoising of Microseismic Data |
| title_full | A Nonparametric Method for Automatic Denoising of Microseismic Data |
| title_fullStr | A Nonparametric Method for Automatic Denoising of Microseismic Data |
| title_full_unstemmed | A Nonparametric Method for Automatic Denoising of Microseismic Data |
| title_short | A Nonparametric Method for Automatic Denoising of Microseismic Data |
| title_sort | nonparametric method for automatic denoising of microseismic data |
| url | http://dx.doi.org/10.1155/2018/4367201 |
| work_keys_str_mv | AT pinganpeng anonparametricmethodforautomaticdenoisingofmicroseismicdata AT liguanwang anonparametricmethodforautomaticdenoisingofmicroseismicdata AT pinganpeng nonparametricmethodforautomaticdenoisingofmicroseismicdata AT liguanwang nonparametricmethodforautomaticdenoisingofmicroseismicdata |