Seismic Data Separation Based on the Equidistant-Spectral Constrained Morphological Component Analysis

During seismic acquisition, the received seismic data typically comprise many components, such as effective reflections and various interferences. Some components, such as industrial electrical interference and traffic vibrations, manifest as the equidistant narrowband discrete spectra (ENBD-spectra...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-12
Main Authors: Wang, Xiaokai, Cui, Chunmeng, Liu, Dawei, Liu, Pu, Shi, Zhensheng, Chen, Wenchao
Format: Article
Language:English
Subjects:
Components
Constraints
Continuous wavelet transform (CWT)
Data analysis
Datasets
Dictionaries
discrete Fourier transform (DFT)
Effectiveness
Electromagnetic interference
equidistant narrowband discrete spectra (ENBD-spectra)
Geoscience and remote sensing
High speed rail
Interference
Locomotives
morphological component analysis (MCA)
Morphology
Narrowband
Noise
Rail transportation
Seismic data
Seismic response
Separation
Sparse approximation
Spectra
Synthetic data
Vibrations
Citations: Items that this one cites
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Summary:During seismic acquisition, the received seismic data typically comprise many components, such as effective reflections and various interferences. Some components, such as industrial electrical interference and traffic vibrations, manifest as the equidistant narrowband discrete spectra (ENBD-spectra) in the frequency domain. Morphological component analysis (MCA) is widely used for separating different component from complicated seismic data. Therefore, it has been successfully used to extract the narrowband components from seismic data. However, the conventional MCA method overlooks equidistant feature of ENBD-spectra component in seismic data separation. In this study, we propose an improved MCA method that uses the interval between neighboring spectrum peaks as a constraint to separating the data with ENBD-spectra component. Two types of seismic datasets are used to show the proposed MCA's effectiveness. The first type of dataset contains industrial electrical interference, while another type of dataset contains high-speed train (HST)-induced seismic signals. Both synthetic data examples and real data examples show that the proposed method has better performance in separating the seismic data with ENBD-spectra component and keeping the fidelity of separation compared with the conventional MCA method.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3420700