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Nonconvex Optimization-Based Inverse Spectral Decomposition
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 81st EAGE Conference and Exhibition 2019, Jun 2019, Volume 2019, p.1 - 5
Abstract
Inverse spectral decomposition could be utilized to obtain a high-resolution time-frequency map via the inversion strategy. An analytic signal is disintegrated as a coefficient matrix whose elements represent the weights of the wavelet components with the different dominant frequencies and the time location in the wavelet library. By using sparse constraint, a high quality inverse decomposition result could be generated. In this paper, a modified ISD technique based on nonconvex optimization algorithm is proposed to pursue a sparser coefficient solution with decreasing the redundant information. New approach applies the lp (0 < p < 1) penalty term to build an accurate mapping relationship between the original signal and its time-frequency spectrum. This adequate regularization in reconstructed function serves as a better alternative to the l1 norm one in conventional ISD. Lower signal to noise ratio and much weaker incoherence of wavelet library cannot impact on the accuracy of output with the new ISD. Synthetic data test with the nonconvex optimization-based ISD are applied to demonstrate the performance gaps. In real application area, the blended image with instantaneous spectral attribute volumes produced by new approach assists the identification of the geological anomalous body, which is verified via a physical model data.