Publication Date:
2019
Description:
〈span〉〈div〉Summary〈/div〉We propose a new Bayesian method to reveal the 〈span〉Vs〈/span〉 structure of the near surface of the earth using spatial autocorrelation (SPAC) functions and apply this new method to synthetic, broadband, and geophone datasets. The principle of SPAC is introduced, and an implementation of the Bayesian Monte Carlo inversion (BMCI) for modeling SPAC coherency functions is described. To demonstrate its effectiveness, BMCI is applied to synthetic tests, data from 14 SPAC array sites in the Salt Lake Valley (SLV), Utah, and two arrays (one broadband and one geophone) located in south central Utah. The 〈span〉Vs〈/span〉 models derived from previous SPAC analysis of the 14 SLV sites differ by 10 per cent at most from those determined by BMCI and lie within uncertainties determined for the BMCI models. These agreements demonstrate the effectiveness of the BMCI method. The synthetic tests and applications to the SLV SPAC data show BMCI has great potential to resolve 〈span〉Vs〈/span〉 structure down to at least 400 m. To achieve resolution for deeper 〈span〉Vs〈/span〉 structure, longer duration deployments, wider array apertures, and additional seismometers or geophones can be employed. Additionally, when the target frequencies are greater than 0.1 Hz, there is no apparent disadvantage in using geophone data for BMCI compared to broadband data. Most significantly, BMCI places a quantifiable constraint on the uncertainties of the 〈span〉Vs〈/span〉 models as well as 〈span〉Vs30〈/span〉.〈/span〉
Print ISSN:
2051-1965
Electronic ISSN:
1365-246X
Topics:
Geosciences
Published by
Oxford University Press
on behalf of
The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
