Ambient noise tomography (ANT) is a passive seismic imaging technique that utilizes naturally occurring background vibrations, such as those generated by ocean waves, wind, and human activity, to map subsurface structures. Unlike traditional seismic methods that rely on controlled sources, ANT extracts coherent seismic signals from continuous ambient noise recordings, making it a cost-effective and environmentally friendly approach. By cross-correlating noise data between pairs of seismic sensors, the method reconstructs surface wave propagation patterns, which are then inverted to estimate shear-wave velocity (Vs) variations in the subsurface. This allows for the creation of detailed 3D geophysical maps that reveal variations in rock stiffness, sediment thickness, and other critical properties without the need for artificial excitation sources.
One of the key strengths of ambient noise tomography is its ability to produce high-resolution 3D velocity models of the shallow to mid-crustal subsurface. The technique is particularly useful in urban environments, where active-source seismic surveys may be impractical due to logistical or regulatory constraints. By deploying arrays of seismometers over a target area, ANT can generate dense spatial coverage, enabling the detection of subtle velocity contrasts associated with faults, bedrock topography, or sediment-filled basins. The resulting 3D maps are valuable for applications such as seismic hazard assessment, geothermal reservoir characterization, and groundwater studies, where understanding subsurface stiffness and heterogeneity is essential for risk mitigation and resource management.
The integration of ambient noise tomography with other geophysical methods enhances its utility in constructing comprehensive 3D subsurface models. For example, combining ANT with electrical resistivity or gravity data can help differentiate between lithological variations and fluid content, reducing interpretation ambiguities. Additionally, time-lapse ANT monitoring can track dynamic changes in the subsurface, such as groundwater depletion or induced seismicity effects. As computational power and signal processing techniques advance, the resolution and reliability of ANT-derived 3D maps continue to improve, making it an increasingly powerful tool for geophysical imaging in both academic research and engineering applications.