A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
Applied Sciences
; 12(13):6470, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1933958
ABSTRACT
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 r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w 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.
Sciences: Comprehensive Works; seismic signal; wavelet transform; wavelet selection; CNN; RNN; Mean square errors; Data acquisition; Waveforms; Seismic studies; Datasets; Deep learning; Mathematical analysis; Wavelet transforms; Seismic stability; Data smoothing; Decomposition; Signal processing; Electrocardiography; Mining; Monitoring systems; Data compression; Correlation coefficients; Correlation coefficient; Proteins; Remote sensing; Signal to noise ratio; Fault diagnosis; Classification; Voice recognition; Picking; Signal classification; Earthquakes; Methods; Information processing; Coronaviruses; Data sets
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Applied Sciences
Year:
2022
Document Type:
Article
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