A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet se...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6470 |
| _version_ | 1797481011742769152 |
|---|---|
| author | Zhengxiang He Shaowei Ma Liguan Wang Pingan Peng |
| author_facet | Zhengxiang He Shaowei Ma Liguan Wang Pingan Peng |
| author_sort | Zhengxiang He |
| collection | DOAJ |
| description | Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance <i>r</i> is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of <i>r</i> are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value <i>μ</i> and variance value <i>σ</i><sup>2</sup> of that set, we define the decomposition stability <i>w</i> as <i>μ</i>/<i>σ</i><sup>2</sup>. Then, the wavelet that maximizes <i>w</i> for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals. |
| first_indexed | 2024-03-09T22:08:21Z |
| format | Article |
| id | doaj.art-51f90c6fe767434ab8269cca4f19437b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:08:21Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-51f90c6fe767434ab8269cca4f19437b2023-11-23T19:37:15ZengMDPI AGApplied Sciences2076-34172022-06-011213647010.3390/app12136470A Novel Wavelet Selection Method for Seismic Signal Intelligent ProcessingZhengxiang He0Shaowei Ma1Liguan Wang2Pingan Peng3School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaWavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance <i>r</i> is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of <i>r</i> are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value <i>μ</i> and variance value <i>σ</i><sup>2</sup> of that set, we define the decomposition stability <i>w</i> as <i>μ</i>/<i>σ</i><sup>2</sup>. Then, the wavelet that maximizes <i>w</i> for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.https://www.mdpi.com/2076-3417/12/13/6470seismic signalwavelet transformwavelet selectionCNNRNN |
| spellingShingle | Zhengxiang He Shaowei Ma Liguan Wang Pingan Peng A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing Applied Sciences seismic signal wavelet transform wavelet selection CNN RNN |
| title | A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing |
| title_full | A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing |
| title_fullStr | A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing |
| title_full_unstemmed | A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing |
| title_short | A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing |
| title_sort | novel wavelet selection method for seismic signal intelligent processing |
| topic | seismic signal wavelet transform wavelet selection CNN RNN |
| url | https://www.mdpi.com/2076-3417/12/13/6470 |
| work_keys_str_mv | AT zhengxianghe anovelwaveletselectionmethodforseismicsignalintelligentprocessing AT shaoweima anovelwaveletselectionmethodforseismicsignalintelligentprocessing AT liguanwang anovelwaveletselectionmethodforseismicsignalintelligentprocessing AT pinganpeng anovelwaveletselectionmethodforseismicsignalintelligentprocessing AT zhengxianghe novelwaveletselectionmethodforseismicsignalintelligentprocessing AT shaoweima novelwaveletselectionmethodforseismicsignalintelligentprocessing AT liguanwang novelwaveletselectionmethodforseismicsignalintelligentprocessing AT pinganpeng novelwaveletselectionmethodforseismicsignalintelligentprocessing |