Automated Data Selection in the Tau–p Domain: Application to Passive Surface Wave Imaging

Abstract

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.

High‐Resolution Ambient Noise Imaging of Geothermal Reservoir Using 3C Dense Seismic Nodal Array and Ultra‐Short Observation

“…As for the central station (Figure 2b), it shows dominant frequencies around 10 Hz which is similar as that in urban area, and the distinct daily pattern which reflects much regular human activities in the peaceful rural town compared with that on the north station. Several long duration and very narrow‐band signals, visible as horizontal lines or spikes (as indicated by the double arrow around 4.2 Hz), were also observed; these seismic waves are most probably excited by rotating machinery operating at fixed frequencies, like electrical motors and gearboxes of industrial machinery (Cheng et al., 2019; Groos & Ritter, 2009; Plesinger & Wielandt, 1974). As for the south station located in the mountain area (Figure 2c), the PSD is generally 10 dB lower than that in the central town area, and the weak daily pattern indicates the observed noise energy from the distant human activities.…”

Seismic characterization of a rapidly-rising jökulhlaup cycle at the A.P. Olsen Ice Cap, NE-Greenland

“…Roux and others, 2005; Draganov and others, 2009; Nakata and others, 2015). Therefore, surface-based seismic imaging using interferometry mostly involves the retrieval and inversion of surface-wave dispersion for shear-wave velocity models (Bensen and others, 2007; Behm and others, 2014; Hannemann and others, 2014; Cheng and others, 2018, 2019). The sensitivity of the EGF to small medium changes with time has been demonstrated in different environments and at different scales (e.g.…”

Artifacts in high-frequency surface wave dispersion imaging

“…Compared with the active-source methods, the passive-source surface wave methods have the advantage of extending the dispersion measurement to lower frequencies, but suffer from incoherent noise, particularly at higher frequencies, due to the unknown distribution of ambient noise sources (Cheng et al, 2018b(Cheng et al, , 2019. In this study, we summarize these frequently observed imaging artifacts into two groups:…”

“…MAPS) with different virtual-source gathers on attenuation of the radial pattern artifacts. The dataset was first reported by Cheng et al (2019), which was collected along a busy railway over 30-min using a 24-channel linear array. The spatial interval is 10 m. Ambient noise interferometry is applied to retrieve empirical Green's functions.…”