On a five-layer Goupillaud benchmark, a normalized reduced-operator objective outperforms classical data misfit in several regimes and stays competitive in the rest.
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Reduced-order modeling for electromagnetic inverse problems: a layered medium benchmark
On a five-layer Goupillaud benchmark, a normalized reduced-operator objective outperforms classical data misfit in several regimes and stays competitive in the rest.