In the recent decades, passive surface wave methods have gained much attention in the near-surface community due to their ability to retrieve low-frequency surface wave information. Temporal averaging over a sufficiently long period of time is a crucial step in the workflow to fulfill the randomization requirement of the stationary source distribution. Because of logistical constraints, passive seismic acquisition in urban areas is mostly limited to short recording periods. Due to insufficient temporal averaging, contributions from non-stationary sources can smear the stacked dispersion measurements, especially for the low-frequency band. We formulate a criterion in the tau–p domain for selective stacking of dispersion measurements from passive surface waves and apply it to high-frequency (> 1 Hz) traffic noise. The criterion is based on the automated detection of input data with a high signal-to-noise ratio in a desired velocity range. Modeling tests demonstrate the ability of the proposed criterion to capture the contributions from the non-stationary sources and classify the passive surface wave data. A real-world application shows that the proposed data selection approach improves the dispersion measurements by extending the frequency band below 5 Hz and attenuating the distortion between 6 and 13 Hz. Our results indicate that significant improvements can be obtained by considering tau–p-based data selection in the workflow of passive surface wave processing and interpretation.

Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals' acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms' domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: The first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.