Fast and semi-automatic S-wave and P-wave velocity estimations from landstreamer data: a field case from the Middle East

Abstract

Seismic surface and body wave analyses are powerful tools for the geotechnical characterization of sites. The use of landstreamers facilitates the acquisition of dense data sets over large areas. However, efficient processing workflows are needed to estimate 3D velocity models from these massive data sets. For surface wave analysis, the manual picking of dispersion curves (DCs) of large data sets is very time-consuming, whereas the accuracy can be biased by operator choices. We apply a semi-automatic workflow for the analysis, processing, and interpretation of a large-scale landstreamer data set acquired for engineering purposes in the Middle East. The workflow involves the application of a validated automatic DC picking algorithm, and the transformation of the DCs into S- and P-wave velocity models through the wavelength-depth technique. The method has a high level of automation, is data driven and does not require extensive data inversion. Another remarkable benefit is that the auto-picking is more than 1,000 times more efficient than standard manual picking and the estimated velocities are in good agreement with available geotechnical and geophysical information. We conclude that the semi-automatic approach may represent a fast and straightforward method suitable for both research and industrial projects, thus enhancing further collaborations and developments.