Ice volume and basal topography estimation using geostatistical methods and ground-penetrating radar measurements: application to the Tsanfleuron and Scex Rouge glaciers, Swiss Alps

Abstract

Abstract. Ground-penetrating radar (GPR) is widely used for determining mountain glacier thickness. However, this method provides thickness data only along the acquisition lines, and therefore interpolation has to be made between them. Depending on the interpolation strategy, calculated ice volumes can differ and can lack an accurate error estimation. Furthermore, glacial basal topography is often characterized by complex geomorphological features, which can be hard to reproduce using classical interpolation methods, especially when the field data are sparse or when the morphological features are too complex. This study investigates the applicability of multiple-point statistics (MPS) simulations to interpolate glacier bedrock topography using GPR measurements. In 2018, a dense GPR data set was acquired on the Tsanfleuron Glacier (Switzerland). These data were used as the source for a bedrock interpolation. The results obtained with the direct-sampling MPS method are compared against those obtained with kriging and sequential Gaussian simulations (SGSs) on both a synthetic data set – with known reference volume and bedrock topography – and the real data underlying the Tsanfleuron Glacier. Using the MPS modeled bedrock, the ice volume for the Scex Rouge and Tsanfleuron glaciers is estimated to be 113.9 ± 1.6 million cubic meters. The direct-sampling approach, unlike the SGS and kriging, allowed not only an accurate volume estimation but also the generation of a set of realistic bedrock simulations. The complex karstic geomorphological features are reproduced and can be used to significantly improve for example the precision of subglacial flow estimation.

Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)

“…Exposed topography has previously been used to perform deterministic interpolations (Clarke et al, 2009). However, deglaciated topography has not been used to stochastically simulate subglacial topography, until the very recent alpine glacier study by Neven et al (2021) using multiple-point geostatistics.…”

Discrete stochastic inversion: getting closer to hydrogeological applications

“…This test case uses the methodology presented by Neven et al (2021), which was employed to tune DS parameters for simulating the Tsanfleuron glacier bedrock geometry. The training image is the topography of Tsanfleuron glacier and the exposed bedrock after glacier retreat (Fig.…”

“…The training image is the topography of Tsanfleuron glacier and the exposed bedrock after glacier retreat (Fig. 3.5) taken from Neven et al (2021). Then, an area of 200 × 200 pixels is cut out from the TI, except for two perpendicular crossing lines (as an analog for GPR data), which constitute the conditioning data.…”

“…When the bedrock elevations are first averaged over all locations for single realizations, and this distribution is compared to averaged true elevation, and then CRPS score computed; the MVE score is formed. More details and rationale behind the scores were described by Neven et al (2021).…”

“…DS has been used and tested in some hydrogeological inversion frameworks, such as posterior population expansion (Jäggli et al 2017;Jäggli et al 2018), iterative spatial resampling (Mariethoz et al 2010c), and Ensemble Smoother with multiple data assimilation (Lam et al 2020). The applications of DS go beyond hydrogeology, they also include reservoir modeling (petroleum engineering), ore reserve estimation (Dagasan et al 2018), climate modeling, glacier bedrock simulations (Neven et al 2021).…”