Trans-dimensional inversion of multimode seismic surface wave data from a trenched distributed acoustic sensing survey. Issue 2 (15th March 2023)

• Record Type: Journal Article
• Authors: Qu, Luping; Dettmer, Jan; Hall, Kevin; Innanen, Kristopher A; Macquet, Marie; Lawton, Donald C
• Is Part Of: Geophysical journal international. Volume 234:Issue 2(2023)
• Page Start: 1051
• Page End: 1062
• Publication Date: 2023-03-15
• Subjects: Inverse theory -- Probability distributions -- Statistical methods -- Surface waves and free oscillations; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggad112
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Seismic data acquired from surface-deployed distributed acoustic sensing (DAS) fibre are broad band and typically dense spatially sampled. Corresponding to these features, compared with geophone data, the low-frequency components in DAS data show higher signal-to-noise ratio and multimode dispersion curves by broad-band DAS data exhibit a higher resolution, which increases the investigation depth of near-surface structures and enhances identification and picking of dispersion curves, respectively. Therefore, DAS data are ideal for the estimates of reliable and highly resolved near-surface velocity profiles. As surface-wave dispersion inversion (SWD) is a natural scheme for near-surface investigation, in this study we have formulated a DAS-SWD inversion in which multiple SWD modes are extracted from the DAS data, and are used as input to a trans-dimensional (TD) inversion procedure, in which the number of subsurface layers is treated as an unknown. Vibroseis data with a minimum frequency of 1 Hz were sensed along a horizontal surface trench as part of a baseline seismic survey carried out by the University of Calgary at the Containment and Monitoring Institute Field Research Station in Newell County, Alberta, Canada. These surface DAS data readily permit the picking of multimode dispersion curves, which are observed to enhance velocity profile resolution in both shallow and deep regions of the near-surface simultaneously, with the TD algorithm adapting the model to reflect this improved resolution. To avoid collecting abnormal model samples with thin-interleaved high- and low-velocity layers based on the known geological information of the field site, we employed constraints that preclude the structures that have velocity drops over 100 m s −1 along depth. Data errors are estimated via a non-parametric iterative process in terms of covariance matrices that include off-diagonal elements. Synthetic examples show that SWD with higher-order modes provides additional constraints on the structure and accurate noise estimation. Inversion of the field data resulted in high-resolution estimates of shear wave velocity as a function of depth throughout the top 120 m of the subsurface. The inferred structure is consistent with existing estimates of the regional lithology but resolves additional layers between 1- and 50-m depth.