The use of surface wave testing for near‐surface engineering applications has increased in recent years. Typical surface wave analysis is based on the dispersion of surface waves in one‐dimensional layered models. One‐dimensional models are inappropriate for measurements at sites with appreciable lateral variability, a likely scenario in many engineering applications. Use of such models can subsequently undermine the reliability and accuracy of the surface wave results. Full waveform inversion (FWI) is a high‐resolution imaging technique that is proven to outperform the conventional dispersion‐based analysis of surface waves. Much of the near‐surface literature has focused on full waveform inversion of Rayleigh waves developed by the interaction of primary‐ and vertically polarized shear waves (P‐SV), and the capabilities of surface waves generated by horizontally polarized shear waves (Love waves) in mapping near‐surface spatial variabilities have not been fully explored. In this numerical study, full waveform inversion of Rayleigh and Love waves was performed on two different spatially correlated Gaussian random fields (mean
Surface waves are critical in detecting and locating seismic sources that do not produce much high-frequency radiation. For such sources, typical approaches using body waves for detecting and locating earthquakes are less effective. Slow earthquakes and exotic seismic sources often have this seismic radiation characteristic, and array analyses of surface waves recorded on global and regional seismic networks have proven effective in recognizing such sources. Most approaches have relied on Rayleigh waves, whereas Love waves have rarely been used. Here we develop a new approach using multiscale arrays to detect and locate seismic sources with both Love and Rayleigh surface waves. The method first forms three-station subarrays and then uses three-component records of the stations to independently estimate three sets of surface wave propagation directions and centroid arrival times. The subarray estimates are then assembled to locate seismic sources and their origin times. We find that using multiple, disconnected global networks improves location accuracy and that using both types of surface waves can enhance detection sensitivity and robustness.
more » « less- Award ID(s):
- 2143413
- PAR ID:
- 10429304
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 234
- Issue:
- 3
- ISSN:
- 0956-540X
- Format(s):
- Medium: X Size: p. 2394-2410
- Size(s):
- p. 2394-2410
- Sponsoring Org:
- National Science Foundation
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