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In this article, the use of coherency measures in seismic signal processing is reviewed, along with the introduction of higher-resolution parameter estimation methods. The actual problem analyzed is that of separating diffractions from reflections and utilizing them to perform higher-resolution imaging of small-scale subsurface structures. The main idea is that diffracted waves can be described by a modified moveout equation normally employed by the common reflection surface (CRS) technique. A number of coherency measures have been proposed to assess how well a moveout (defined by some trial parameters) approximates a target signal. Traditional methods using semblance often fail in cases of interfering events. This fact has motivated the investigation of alternative coherency measures based on higher-resolution techniques like multiple signal classification (MUSIC), eigenvector (EV), and minimum variance (MV). Here, the various algorithms are tested employing controlled seismic data from the Marmousi model as well as field data acquired by a ground-penetrating radar (GPR). It is found that the MUSIC algorithm provides the best result slightly ahead of EV and with MV falling somewhere between these two techniques with the more standard approach based on semblance and time migration.