Ambient Noise Beamforming with Joint DAS and Three-Component (3C) Seismic Arrays

Seismic ambient noise analysis has become an important tool for subsurface characterization, offering a cost-effective alternative to active sources and enabling continuous monitoring. Array-based techniques such as beamforming are central to ambient noise analysis, allowing the estimation of wavefield properties such as propagation direction and phase velocity. Traditionally, beamforming has been applied either to vertical-component array data, particularly for surface-wave analysis, or to three-component (3C) seismic arrays, which allow for polarization and wave-type discrimination.

More recently, distributed acoustic sensing (DAS) has emerged as a powerful tool for ambient noise studies, providing dense spatial sampling and large apertures at relatively low per-channel cost. However, DAS measurements are primarily sensitive to axial strain and can therefore be interpreted as effectively single-component observations. As a result, DAS arrays deployed along a single line cannot leverage the benefits of 3C beamforming, such as polarization analysis and wave-type identification. Conversely, sparse 3C arrays provide polarization information but are often limited in wavenumber resolution due to restricted aperture and station spacing.

In this study, we develop and test a joint beamforming approach that combines DAS and 3C seismic observations in a unified framework. The joint beamformer is constructed by combining normalized beam power estimates from DAS-only and 3C-only beamforming, enhancing coherent signals that are consistent across both datasets while suppressing incoherent or aliased energy. The performance of the joint approach is evaluated using numerical simulations in layered elastic media. Systematic tests are carried out for different array geometries and station spacings to investigate their effects on aliasing, resolution, and information gain. The results show that the joint beamformer improves the stability of the results, particularly in cases where DAS-only or 3C-only beamforming suffers from aliasing or limited resolution. Finally, the method is applied to a real test dataset to demonstrate its applicability under realistic noise conditions.

Our study suggests that joint DAS–3C beamforming provides a robust framework for ambient noise analysis, offering improved wavefield characterization compared to single-sensor approaches and highlighting the potential of hybrid array designs for future seismic monitoring applications