SUMMARY: Ambient noise surface wave methods have gained much attention among geophysical and civil engineering communities because of their capability of determining near-surface shear wave velocities in highly populated urban areas. Higher mode information of surface waves is important in dispersion curve inversion for shear wave velocity structure. The frequency–Bessel (F-J) transform method is an effective tool for multimode surface wave extraction, which has been applied to multiscale investigations of the Earth structure. The measured dispersion energy with the F-J method, however, would usually be contaminated by a type of ‘crossed’ artefacts at high frequencies, which are caused by spatial aliasing and bidirectional velocity scan of dispersion analysis methods. The ‘crossed’ artefacts usually cross and smear the true dispersion energy in the frequency–velocity domain. We propose a modified F-J (MFJ) transform method in which the Bessel function is replaced by the Hankel function for dispersion analysis of empirical Green's function. The MFJ method performs a unidirectional velocity scanning on the outgoing wave to avoid the ‘crossed’ artefacts. Synthetic and real-world examples demonstrate the effectiveness of the proposed MFJ method in improving the accuracy of Rayleigh wave multimode dispersion measurements.

Tomographic imaging based on long-term ambient seismic noise measurements, mainly the phase information from surface waves, has been shown to be a powerful tool for geothermal reservoir imaging and monitoring. In this study, we utilize seismic noise data from a dense nodal array (192 3C nodes within 20 ) over a ultra-short observation period (4.7 days) to reconstruct surface waves and determine the high-resolution (0.2 km) three-dimensional (3-D) S wave velocity structure beneath a rural town in Zhejiang, China. We report the advantage of cross-coherence over cross-correlation in suppressing pseudo-arrivals caused by persistent sources. We use ambient noise interferometry to retrieve high quality Rayleigh waves and Love waves. Body waves are also observed on the R-R component interferograms. We apply phase velocity dispersion measurements on both Rayleigh waves and Love waves and automatically pick more than 23,000 dispersion curves by using a Machine Learning technique. 3-D surface wave tomographic results after depth inversion indicate low-velocity anomalies (between −1% and −4%) from the surface to 2 km depth in the central area. Combined with the conductive characteristics observed on resistivity profile, the low-velocity anomalies are inferred to be a fluid saturated zone of highly fractured rock. Joint interpretation based on horizontal-to-vertical spectral ratio (HVSR) measurements, and existing temperature and fluid resistivity records observed in a nearby well, suggests the existence of the high-temperature geothermal field through the fracture channel. Strong correlation between HVSR measurements and the S wave velocity model highlights the potential of extraction of both amplitude and phase information from ambient noise.

SUMMARY: Seismic interferometry is becoming increasingly popular in urban areas due to its ability to retrieve high-frequency surface waves from abundant anthropogenic seismic noise, hence the need for advanced processing schemes in resolving complex environments. Stacking noise cross-correlation functions is an essential step for the successful retrieval of surface waves and some nonlinear methods are developed for attenuating incoherent noise; however, these methods are susceptible to waveform distortions. In addition, a lot of attention has been focused on the improvement of the pairwise noise cross-correlation functions, while the spatial coherency of waves is less utilized. We obtain the multichannel coherency by summing the local phase coherencies in a time window and propose the multichannel-coherency-weighted stack method for accelerating the retrieval of high-frequency surface waves. A synthetic test and a real-world three-component example demonstrate the superiority of the proposed method over both the linear stack and the phase-weighted stack methods in obtaining cleaner surface waves and more accurate dispersion measurements. Our method is not limited by waveform distortions owing to its linearity. Furthermore, the proposed method has the potential to be extended to body wave retrieval from ambient noise by adjusting its parameters.

Seismic interferometry is widely used to image and monitor earth structures at different scales by extracting an empirical Green’s function (EGF) from the crosscorrelation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. However, EGFs may be incorrectly estimated and lead to a misunderstanding of subsurface structures because the distribution of ambient noise sources is neither isotropic nor stationary. To extract reliable EGFs from the nonideal ambient noise data, we develop a polarization-based azimuth filter (PAF) to attenuate the ambient noise energy of nonstationary sources to improve the quality of crosscorrelation functions. The PAF is a time-frequency domain azimuth filter for 3C seismic data, which uses time-frequency polarization analysis to measure the azimuths of ambient noise signals and then filters the 3C data to obtain the signals from desired directions. Based on the stationary-phase theory, we use the PAF to extract the ambient noise signals from stationary-phase zones, which improves the quality of the crosscorrelation function and eliminates the requirement for long observations. A synthetic test and two short-time engineering examples demonstrate that uneven source distributions might cause serious spurious signals in EGFs, and we demonstrate the improvement in EGFs using our method. In addition, the PAF may allow for the complete separation of the fundamental and higher modes of Rayleigh waves, which uncovers more information implicit in the ambient noise data.