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Unsupervised Global Identification of Diffractions based on Local Wavefront Measurements

Multi-parameter stacking schemes like the common-reflection-surface (CRS) stack have shown to yield reliable results even for strongly noise-contaminated data. This is particularly useful for low-amplitude events such as diffractions, but also in passive seismic settings. As a by-product to a zero-offset section with a significantly improved signal-to-noise ratio, the CRS stack also extracts a set of physically meaningful wavefront attributes from the seismic data, which are a powerful tool for data analysis. Although the attributes vary laterally along the events, an analysis of their local similarity allows the global identification of measurements, which stem from the same diffractor or passive source, i.e., from the same region in the subsurface. In this work, we present a fully automatic scheme to globally identify and tag diffractions in simple and complex data by means of local attribute similarity. Due to the the fact that wave propagation is a smooth process and due to the assumption of only local attribute similarity, this approach is not restricted to settings with moderate subsurface heterogeneity.