Ambient Noise Tomography in Seismic Imaging

Ambient noise tomography harnesses background seismic noise recorded continuously by arrays of seismometers to extract empirical Green’s functions through cross-correlation. This approach inverts surface wave dispersion and ambient seismic hum to map subsurface shear wave velocity variations at scales from metres to thousands of kilometres. Recent methodological refinements in time–frequency phase-weighted stacking and machine-learning-augmented inversion have elevated spatial resolution, enabling delineation of crustal layering, fluid- and melt-bearing zones, fault structures and lithospheric heterogeneity. Unlike earthquake-based tomography, ambient noise imaging offers uniform sampling, reduced dependence on seismicity distribution and cost-effective deployment of large-N arrays, proving invaluable for mineral exploration, hydrocarbon assessment, volcanic hazard monitoring and global mantle studies. Continued integration with asynchronous networks, trans-dimensional inversion schemes and cross-disciplinary datasets has fostered high-fidelity three-dimensional models that reveal fine-scale features such as sedimentary basins, mineral deposits, magmatic sills and lithospheric thinning. The global adoption of this technique underscores its transformative role in seismic imaging across scales, from engineered reservoirs to continental dynamics.

Research from Nature Portfolio

Advances in large-scale ambient noise imaging have produced an updated three-dimensional shear-velocity model of the Australian crust by combining nearly three decades of continuous recordings across over 1 600 stations. An optimised workflow integrates asynchronous array data to achieve ~1° lateral resolution, revealing low-velocity sedimentary basins, faster velocities under mineralised zones and refined crust–mantle transition characterisation, with implications for undercover mineral exploration. Separately, a machine learning-based travel time tomography approach applied to a dense urban array in southern California exploits ambient noise processed on large-N deployments. A locally sparse travel time algorithm learns geophysical feature dictionaries from the data itself, yielding a high-resolution Rayleigh wave phase speed map that isolates aquifer structures with superior detail compared to conventional methods.

Ambient Noise Tomography in Seismic Imaging publication trend

The graph below shows the total number of articles in ambient noise tomography in seismic imaging across all publications each year (not limited to Nature Index journals).

Technical terms

Ambient noise tomography: Imaging method using continuous seismic noise to extract subsurface wave propagation information.
Cross-correlation: A signal processing technique that computes the similarity between two noise records to retrieve waveforms equivalent to those from a virtual source.
Dispersion curve: Frequency-dependent variation of seismic wave speed, used to infer subsurface velocity structure.
Shear wave velocity (Vs): Speed at which transverse seismic waves travel, sensitive to material rigidity and fluid content.
Anisotropy: Directional dependence of wave velocity, indicating aligned fractures, mineral fabrics or layered structures.

References

1. Next-generation seismic model of the Australian crust from synchronous and asynchronous ambient noise imaging. Nature Communications (2023).
2. Extreme seismic anisotropy indicates shallow accumulation of magmatic sills beneath Yellowstone caldera. Earth and Planetary Science Letters (2023).
3. Study of Shale Gas Source Rock S-Wave Structure Characteristics via Dense Array Ambient Noise Tomography in Zhangjiakou, China. Remote Sensing (2025).
4. Global tomography using seismic hum. Geophysical Journal International (2015).
5. Trans‐Dimensional Surface Reconstruction With Different Classes of Parameterization. Geochemistry Geophysics Geosystems (2019).
6. High-resolution seismic tomography of Long Beach, CA using machine learning. Scientific Reports (2019).
7. Tomography of crust and lithosphere in the western Indian Ocean from noise cross-correlations of land and ocean bottom seismometers. Geophysical Journal International (2019).