Estimating primaries from passive seismic data
Hao Cheng 1 2 De-Li Wang 1 Fei Feng 1 Heng Zhu 11 Faculty of Geo Exploration Science and Technology, Jilin University, Changchun 130026, China.
2 Corresponding author. Email: chenghao100@gmail.com
Exploration Geophysics 46(2) 184-191 https://doi.org/10.1071/EG14079
Submitted: 30 July 2014 Accepted: 29 October 2014 Published: 17 December 2014
Abstract
Passive seismic sources can generally be divided into transient sources and noise sources. Noise sources are particularly the continuous, random small bursts, like background noise. The virtual-shot gathers obtained by the traditional cross-correlation algorithm from passive seismic data not only contain primaries, but also include surface-related multiples. Through estimating primaries by sparse inversion, we can directly obtain primaries from passive seismic data activated by transient sources, which are free of surface-related multiples. The problem of estimating primaries from passive seismic data activated by noise sources has not been discussed to date. First, by introducing the optimisation problem via the L1-norm constraint, this paper makes the traditional method of estimating primaries by sparse inversion from passive seismic data activated by transient sources improved, which overcomes the time-window problem. During the sparse inversion, the sparsifying transform, S = C2⊗W, is introduced. In the sparsifying-transform domain, the transformed data is more sparse, so the solution becomes more accurate. Second, this paper proposes estimating primaries from passive seismic data activated by noise sources. In the case of the sparse assumption not holding, we use the least-squares method based on the principle of minimum energy to estimate primaries from passive seismic data using the noise sources. Finally, we compare the primaries estimated from passive seismic data using transient sources and noise sources and analyse the characteristics of the estimated primaries obtained from two passive seismic data.
Key words: 2D curvelet-wavelet transform, least-square, noise sources, passive sources, sparse inversion, transient sources.
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