Migration artifacts & QC

Every migration is an approximation of the earth, and every approximation leaves traces. Production interpreters learn to read migration output for two things at once: the geology underneath, and the artefact pattern that tells them whether the image is trustworthy. This section catalogues the main artefact fingerprints each migration method can produce, and the QC workflow that catches them before they mislead an interpretation.

1. The widget — four canonical artefacts

Left panel: the ideal migration of a single diffractor at (500 m, 900 m) with correct velocity and full aperture — a clean point focus. Right panel: one controlled defect applied. Click the mode buttons to cycle:

• Under-migrated (V_mig too high). The summing hyperbola is narrower than the data hyperbola; the sum does not accumulate and the input hyperbola survives almost untouched. On a production section this looks like diffraction tails that never collapsed.
• Over-migrated (V_mig too low). The summing hyperbola is wider than the data; the sum spreads the focus into an upside-down arc — the classic migration smile. Smiles that cluster near fault edges or truncations flag over-migration.
• Aperture truncated. Only 25 of 81 traces participate in the summation. The focus is still at the apex but smaller smiles appear near the aperture boundary because the hyperbola's tails were clipped. Surveys with short cables show this on their edges.
• Trace-aliased (every 4th trace). Spatial aliasing creates periodic replicas — the focus repeats at the spatial-aliasing frequency of the decimation. You see ghosts of the real event at regular lateral offsets.

2. Common-image gathers (CIGs) — the velocity QC

Pre-stack migrations (PSTM, PSDM, beam, RTM) preserve the offset (or angle) axis. For each CMP position, gather the migrated image over offset: that is a common-image gather. If the migration velocity is correct, the event is flat across offset. If V_mig is too high, the event curves down at far offsets (residual over-migration). If V_mig is too low, it curves up (residual under-migration). The curvature is directly proportional to the velocity error. CIGs are the single most important QC and the driver of the iterative velocity update in §5.9.

3. Angle gathers — the anisotropy QC

Angle gathers are a different rearrangement of the pre-stack migration output: for each image pixel, plot amplitude as a function of the angle between the ray and the normal to the reflector. Angle gathers should be flat with a smooth amplitude vs angle curve. Anomalies at specific angles — systematic lows or flips — reveal anisotropy, thin-bed tuning, or AVO anomalies that an offset gather would smear together.

4. Stack power and S/N

Migration output should have higher zero-offset energy than the input stack because diffractions have been collapsed into reflectors. A drop in zero-offset energy after migration means something has gone wrong — usually an over-migration smearing events into low-amplitude arcs. Production QC always compares pre- and post-migration amplitude spectra and coherence.

5. RTM-specific QC

• Low-frequency backscatter. Strong low-wavenumber content near high-velocity boundaries is the tell. Remove with a Laplacian filter (high-pass the image wavenumbers) or use a Poynting-vector imaging condition.
• Random-boundary reflections. Spurious horizontal events at regular depths. Traced to absorbing boundary conditions; switch to random-boundary or deeper-padding schemes.
• Checkpoint artefacts. If source-wavefield checkpointing is too coarse, faint vertical banding at checkpoint intervals. Increase checkpoint density.

6. Migration stretch

Time-migrated far-offset data shows the same frequency-dependent stretching as NMO-corrected gathers (§2.2): shallow, far-offset samples are stretched toward lower frequencies, hurting stack quality at the top of the section. Solutions: apply a stretch mute (zero samples stretched past some threshold, typically 30–50 %) or migrate in the shot-domain to avoid NMO. Depth migration does not suffer from stretch because the time-to-depth conversion happens inside the operator.

7. The QC workflow

• Look at the stacked image. Look for smiles, remnant hyperbolas, edge artefacts, periodic noise, and low-frequency banding. Map the geometry of each — are smiles where velocities change abruptly? Is the periodic noise at the spatial-aliasing frequency of your shot spacing?
• Compare common-image gathers. For a handful of representative CMPs, plot the image as a function of offset or angle. Look for systematic curvature.
• Check the migration parameters. Is the velocity updated? Is the aperture broad enough? Is the anti-alias filter in the Kirchhoff operator active? Is the dip limit of the one-way scheme honoured?
• Iterate the velocity. Residual moveout on CIGs drives the next velocity model (§5.9). A single pass of migration is rarely enough; 2–5 iterations are typical.
• Compare methods. Run both PSTM and PSDM (or beam and RTM) on a subset. Where the two disagree, the faster method is wrong; where they agree, the image is trustworthy.

Where this goes next

§5.9 closes Part 5 with velocity model building — the iterative loop that takes the residual moveout measured on CIGs, updates the velocity field, re-runs the migration, and repeats until the CIGs are flat. Everything in §5.2–5.7 depends on getting that loop to converge.

References

• Yilmaz, Ö. (2001). Seismic Data Analysis (2 vols.). SEG.
• Etgen, J., Gray, S. H., Zhang, Y. (2009). An overview of depth imaging in exploration geophysics. Geophysics, 74, WCA5.
• Stolt, R. H., Benson, A. K. (1986). Seismic Migration: Theory and Practice. Geophysical Press.
• Claerbout, J. F. (1985). Imaging the Earth’s Interior. Blackwell.