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![]() | Seismic data decomposition into spectral components using regularized nonstationary autoregression | ![]() |
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I have presented a constructive approach to decomposing seismic data
into spectral components with smoothly variable frequencies and
smoothly variable amplitudes. The output of the proposed algorithm is
close to that of empirical model decomposition (EMD) and related
techniques, such as the synchrosqueezing transform (SST), but with a
more explicit control on parameters and more direct access to
instantaneous-frequency and amplitude attributes. The main tool for
the task is regularized nonstationary regression (RNR), which is
applied twice: first to estimate local frequencies by autoregression
(RNAR) and then to estimate local amplitudes. Although all examples
shown in this paper use only 1D analysis, the proposed technique is
also applicable to analyzing 2D or 3D variable-slope seismic events in
the -
domain. Potential applications may include noise
attenuation, data compression, and data regularization.