Adaptive Prefiltering for Nonnegative Discrete Spectrum of Relaxations
Recent developments within the estimation of the discrete spectrum of relaxation frequencies (DSRFs) has opened doors to a lot of sturdy subsurface target discrimination using electromagnetic induction measurements. In specific, a nonnegative least squares DSRF (NNLSQ-DSRF) estimation methodology has been shown to be strong and free from parameter tuning. During this letter, we tend to propose an adaptive prefiltering process to enrich the NNLSQ-DSRF where we try to linearly mix measurements and manufacture a filtered signal that's terribly likely to possess a nonnegative DSRF, plus an enhanced signal-to-noise ratio. Using artificial and field information, we have a tendency to demonstrate that the proposed adaptive prefilter can effectively produce signals with nonnegative DSRFs.
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