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Blocky models via the L1/L2 hybrid norm

Jon Claerbout


This paper seeks to define robust, efficient solvers of regressions of $ L1$ nature with two goals: (1) straightforward parameterization, and (2) ``blocky'' solutions. It uses an $ L1/L2$ hybrid norm characterized by a residual $ R_d$ of transition between $ L1$ and $ L2$ for data fitting and another $ R_m$ for model styling. Both the steepest descent and conjugate direction methods are included. The 1-D blind deconvolution problem is formulated in a manner intended to lead to both a blocky impedance function and a source waveform. No results are given.