A synthetic study to assess the applicability of full-waveform inversion to infer snow stratigraphy from upward-looking ground-penetrating radar data

Lino Schmid,Jürg Schweizer,John Bradford,Hansruedi Maurer

doi:10.1190/geo2015-0152.1

Lino Schmid, Jürg Schweizer + Show 2 more

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https://doi.org/10.1190/geo2015-0152.1

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Journal: GEOPHYSICS	Publication Date: Jan 1, 2016
Citations: 23	License type: cc-by

Affiliation: Boise State University, ETH Zurich

Abstract

Snow stratigraphy and liquid water content are key contributing factors to avalanche formation. Upward-looking ground-penetrating radar (upGPR) systems allow nondestructive monitoring of the snowpack, but deriving density and liquid water content profiles is not yet possible based on the direct analysis of the reflection response. We have investigated the feasibility of deducing these quantities using full-waveform inversion (FWI) techniques applied to upGPR data. For that purpose, we have developed a frequency-domain FWI algorithm in which we additionally took advantage of time-domain features such as the arrival times of reflected waves. Our results indicated that FWI applied to upGPR data is generally feasible. More specifically, we could show that in the case of a dry snowpack, it is possible to derive snow densities and layer thicknesses if sufficient a priori information is available. In case of a wet snowpack, in which it also needs to be inverted for the liquid water content, the algorithm might fail, even if sufficient a priori information is available, particularly in the presence of realistic noise. Finally, we have investigated the capability of FWI to resolve thin layers that play a key role in snow stability evaluation. Our simulations indicate that layers with thicknesses well below the GPR wavelengths can be identified, but in the presence of significant liquid water, the thin-layer properties may be prone to inaccuracies. These results are encouraging and motivate applications to field data, but significant issues remain to be resolved, such as the determination of the generally unknown upGPR source function and identifying the optimal number of layers in the inversion models. Furthermore, a relatively high level of prior knowledge is required to let the algorithm converge. However, we feel these are not insurmountable and the new technology has significant potential to improve field data analysis.

Highlights

Snow stratigraphy, i.e., the thickness and properties of the layers that form the seasonal snow cover, is one of the principal contributing factors considered in avalanche formation (Schweizer et al, 2003)
We investigate effects of liquid water content in the snowpack, the amount of a priori information required for successful full-waveform inversion (FWI), nonlinear effects caused by strong contrasts of the physical snowpack properties, and the ability to detect thin layers
We have investigated whether synthetic Upward-looking groundpenetrating radar (upGPR) data contain enough information for inferring the density, liquid water content, and thicknesses of each layer of a snowpack by FWI

Summary

Introduction

I.e., the thickness and properties of the layers that form the seasonal snow cover, is one of the principal contributing factors considered in avalanche formation (Schweizer et al, 2003). The layering on a pit wall can be characterized with near-infrared photography (Matzl and Schneebeli, 2006), and the liquid water content of the snow layers can be derived with instruments that measure the permittivity of the wet snow, such as the Denoth capacity probe (Denoth et al, 1984; Denoth, 1994) or the Finnish snow fork (Sihvola and Tiuri, 1986). All these measurements are cumbersome and time consuming but can be performed only at accessible locations. Gathering information from avalanche starting zones is not always possible

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