Depth Seismic Imaging: A comparison between poststack wave equation migration and Kirchhoff migration
Keywords:
poststack migration, wave equation, Kirchhoff migration, split-step migrationAbstract
Seismic migration is a key process for obtaining reliable subsurface images in geologically complex areas containing dipping layers, faults, and structural folds. Two of the most commonly used techniques in the industry for migrating seismic data are wave-equation migration and Kirchhoff migration. There is a large number of ethods and variants for both techniques. In this study, we analyze and compare two depth migration algorithms applied to poststack data sets. The first algorithm is the split-step method, which is based on downward continuation of the wavefield while accounting for lateral velocity variations. The second algorithm is Kirchhoff migration, which, although also derived from the wave equation, focuses on summing observed amplitudes along arrivals defined by diffraction curves. The split-step method is implemented using a self-developed code in Julia, while Kirchhoff migration is implemented through the commercial software EPOS. This study aims to analyze in which scenarios it is more convenient to use one method over the other. For this purpose, three poststack data sets are processed using both methods: (1) a synthetic data set generated from a realistic velocity model that includes folds, areas with significant dips, and a structural fault; (2) a real data set from the Austral Basin acquired in Tierra del Fuego; and (3) a second real data set from the Fold and Thrust Belt in the Huallaga Basin, Peru, characterized by large dips. The results show that, overall, both methods successfully generate reliable images of the seismic sections for the three poststack data sets. However, given the nature and approach of each method, the split-step method reconstructs steeper-dipping layers more sharply. Meanwhile, the Kirchhoff method better addresses edge effects and produces slightly superior results in areas with faults.
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Copyright (c) 2025 Lucía Páez Gayone, Juan I. Sabbione, Tomás D'Biassi

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