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| Feature extraction in wavelet domain and its application to shallow seismic data surface-wave suppression |
| Rui-Qing HU1,2, Yan-Chun WANG1,2, Zhan-Wei YUE1,2, Qi WANG1,2 |
1. Key Laboratory of Geo-detection (China University of Geosciences),Ministry of Education,Beijing 100083,China
2. School of Geophysics and Information Technology,China University of Geosciences,Beijing 100083,China |
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Abstract Most of shallow seismic signals have low Signal-to-Noise Ratio (SNR) due to the strong surface wave component,which makes is difficult to establish the velocity field.Conventional methods performed poorly in this situation.To overcome this problem,the authors propose a new method based on the adaptive Morlet wavelet and principal component analysis (PCA) process in wavelet coefficients matrix.The difference of horizontal continuity between effective wave and surface wave makes it possible to extract the features in wavelet coefficients domain.Then the reconstructed signal from weighted features presents a high-quality surface wave suppression.Tests on two sets of real data provide a solid evidence for its feasibility in shallow seismic signal.
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Received: 07 May 2018
Published: 19 December 2018
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