Ambient Noise Tomography
Also known as: ANT
Ambient Noise Tomography (ANT) is a seismic imaging method that extracts surface wave information from long-term records of seismic background noise, enabling high-resolution imaging of crustal and upper mantle structure. Developed by Shapiro, Campillo, and colleagues in 2005, ANT has revolutionized seismic imaging by enabling detailed crustal velocity maps at minimal cost without requiring earthquakes or active sources.
Key highlights
- Passive and cheap: requires no active source, only broadband seismometers; deployment is faster than active-source surveys
- High spatial resolution: dense station arrays and multiple frequencies enable detailed 2D/3D imaging
- Rapid imaging: continuous noise processing means useful results appear within weeks to months of deployment, not years
- Complements body-wave methods: surface waves are sensitive to shear velocity and damping; body waves sense P-velocity. Joint inversion is powerful.
Intuition
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How it works
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When to use it
Use ANT for regional-scale crustal imaging where earthquake catalogs are sparse or where rapid deployment is essential. ANT excels at mapping shallow crustal structure (depths ~1–20 km) and detecting near-surface anomalies. For very-deep structure (mantle), ANT is less sensitive; combine with teleseismic surface waves. ANT is ideal for rapid hazard assessment (earthquake risk), geothermal exploration, and hydrocarbon prospecting.
Strengths & limitations
- Passive and cheap: requires no active source, only broadband seismometers; deployment is faster than active-source surveys
- High spatial resolution: dense station arrays and multiple frequencies enable detailed 2D/3D imaging
- Rapid imaging: continuous noise processing means useful results appear within weeks to months of deployment, not years
- Complements body-wave methods: surface waves are sensitive to shear velocity and damping; body waves sense P-velocity. Joint inversion is powerful.
- Directional dependence: noise sources (ocean, weather) are heterogeneously distributed, biasing wave directions and introducing anisotropy artifacts
- Sensitivity mainly to shallow crust: energy content decreases at longer periods, limiting depth penetration compared to earthquake-based methods
- Non-linear dependence on shear velocity: inversion can be non-unique; multiple velocity models may fit the dispersion data equally well
- Computational expense: cross-correlations of months to years of continuous data at high sampling rates demand significant computational resources
Common pitfalls
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Applications
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Frequently asked
Why does cross-correlation of noise converge to the Green's function?
Mathematical theory (equipartition) states that when two points are in a diffuse wave field (excited equally from all directions), cross-correlation converges to the Green's function. In reality, noise sources are not perfectly diffuse, but sufficient averaging over weeks to months and careful spatial sampling yields robust Green's functions.
What is the difference between ambient noise ANT and earthquake surface waves?
ANT uses continuous background noise passively; earthquake surface waves use energy from distant earthquakes. ANT requires long averaging but no earthquakes; earthquake surface waves are intermittent but can be rapid if a large earthquake occurs nearby. For crustal imaging, ANT provides higher frequency content (shorter periods, finer resolution) than most teleseismic surface waves.
How long must seismometers record for ANT?
Minimum useful stacking window is ~2–4 weeks; 3 months to 1 year gives much better signal. For high-frequency (short period) ANT targeting shallow structure, faster convergence (days to weeks) is possible. For deep mantle imaging, years of data may be needed.
Can ANT detect time-lapse changes in crustal properties?
Yes. Repeated ANT surveys (repeating the deployment over months/years) can detect velocity changes. Applications include CO2 sequestration monitoring, geothermal production changes, and earthquake-triggered stress changes. Changes on the order of 1–2% are detectable with careful processing.
Sources
- 1.Shapiro, N. M., Campillo, M., Stehly, L., & Ritzwoller, M. H. (2005). High-resolution surface-wave tomography from ambient seismic noise. Science, 307(5715), 1615-1618.
- 2.Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., et al. (2008). Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophysical Journal International, 169(3), 1239-1260.
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Cite this page
ScholarGate. (2026, June 3). Ambient Noise Tomography. ScholarGate. https://scholargate.app/geophysics/ambient-noise-tomography