Abstract
AbstractMuLTI (Multimodal Layered Transdimensional Inversion) is a Markov chain Monte Carlo implementation of Bayesian inversion for the probability distribution of shear wave velocity (Vs) as a function of depth. Based on Multichannel Analysis of Surface Wave methods, it requires as data (i) a Rayleigh‐wave dispersion curve and (ii) additional layer depth constraints, the latter we show significantly improve resolution compared to conventional unconstrained inversions. Such depth constraints may be drawn from any source (e.g., boreholes, complementary geophysical data) provided they also represent a seismic interface. We apply MuLTI to a Norwegian glacier, Midtdalsbreen, in which ground‐penetrating radar was used to constrain internal layers of snow, ice, and subglacial sediments, with transitions at 2 and 25.5 m, and whose Vs is assumed to be in the range 500–1,700, 1,700–1,950, and 200–2,800 m/s, respectively. Synthetic modeling demonstrates that MuLTI recovers the true model of Vs variation with depth. Significantly, compared to inversions without depth constraints, in this synthetic case MuLTI not only reduces by about a factor of 10 the error between the true and the best fitting model, but also reduces by a factor of 2 the vertically averaged spread of the distribution of Vs based on the 95% credible intervals. We further show that using frequencies above about 100 Hz lead to unconverged solutions due to mode ambiguities associated with fine spatial structures. For our acquired data on Midtdalsbreen, we use 14‐100 Hz data for which MuLTI produces a large‐scale converged inversion.
Highlights
Many inversions of geophysical data derived from a single type of geophysical instrument are underconstrained, a property that results from limitations of the size and accuracy of the data set, but inherent nonuniqueness: many models of the subsurface may be consistent with the single class of surface-based constraints
Based on Multichannel Analysis of Surface Wave methods, it requires as data (i) a Rayleigh-wave dispersion curve and (ii) additional layer depth constraints, the latter we show significantly improve resolution compared to conventional unconstrained inversions
Our paper focuses on the use of constrained inversions to characterize a glaciated subsurface by inverting Rayleigh wave data sets in the presence of depth constraints here provided by ground-penetrating radar (GPR) data
Summary
Many inversions of geophysical data derived from a single type of geophysical instrument are underconstrained, a property that results from limitations of the size and accuracy of the data set, but inherent nonuniqueness: many models of the subsurface may be consistent with the single class of surface-based constraints. By combining Rayleigh wave observations and depth information from GPR data, constrained inversion offers a powerful way to reduce the ambiguities inherent in single-technique inversions, provided that subsurface interfaces correspond to colocated contrasts in both elastic and electromagnetic properties. This assumption is likely appropriate in (for example) a glaciated environment with snow, ice, and a subglacial substrate (Tsuji et al, 2012); a permafrost environment featuring unfrozen and frozen ground (Kneisel et al, 2008); and hydrological settings such as the imaging of shallow aquifers (Cardimona et al, 1998). We focus on glacial environments, MuLTI can be used in any layered geological environment where electromagnetic and elastic properties change at the same depths
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