Interferometric discrimination of cross-track bed clutter in ice-penetrating radar sounding data

Abstract

The interpretations of relevant interfaces (i.e. the surface and bed) in radar sounding datasets over glaciers and ice sheets are primary boundary conditions in a variety of climate studies and particularly subglacial water routing models. It is therefore necessary to ensure these interpretations are consistent and not affected by cross-track clutter. For the surface interface, interferometry and a family of methods relying on digital elevation models have been used to successfully discriminate cross-track surface clutter. Here we present how interferometry can be applied to the problem of basal clutter from cross-track bed topography. Our approach is based on a comparison of the differential phases of ambiguous reflectors that may represent bed clutter and the differential phase of a reflector in an adjacent area that appears unaffected by basal clutter. The reflector yielding the smallest interferometric phase difference relative to the unambiguous bed reflector is considered to represent its consistent continuation. We successfully demonstrate our approach using 60 MHz center frequency MARFA data collected over Devon Ice Cap in the Canadian Arctic. Finally, we investigate the effects of clutter-affected and interferometry-corrected bed interpretations on ice layer thickness estimates, basal hydraulic head gradients and the potential extent of inferred subglacial water bodies.

Five decades of radioglaciology

“…Just as coherent radar sounders enabled improved along-track resolution and processing gain, the development of systems with multi-channel cross-track arrays improved cross-track resolution, processing gain, clutter discrimination and swath mapping (Gogineni and others, 1998; Paden and others, 2010; Wu and others, 2011; Rodriguez-Morales and others, 2013; Castelletti and others, 2017; Holschuh and others, 2020; Scanlan and others, 2020). This is also true for ground-based multiple input, multipleoutputimplementations of the ‘phase-sensitive’ FMCW radars mentioned above (Young and others, 2018).…”

Radar sounding survey over Devon Ice Cap indicates the potential for a diverse hypersaline subglacial hydrological environment

“…CC BY 4.0 License. reflection was discriminated from cross-track clutter using the results from Scanlan et al (2020). Travel times were then converted to depths using a radar wave velocity in ice of 168.4 m/µs.…”

“…To ensure a continuous transition between the bed DEM and the non-glaciated surrounding topography, land elevations from the ArcticDEM, Polar Geospatial Center from DigitalGlobe Inc. imagery (ArcticDEM) outside of the ice cap were included in the bed DEM generation. A total of 47233 crossover points reveal a mean cross-over error of 17.6 m (median of 7.6 m) and a standard deviation cross-over error of 42.5 m. Sources of errors in the radar-derived bed elevation include uncertainties in the ice surface due to a heterogeneous firn affected by melting/refreezing processes (Rutishauser et al, 2016) that are propagated into the bed elevation, cross-track scattering at the surface and bedrock (Scanlan et al, 2020), and uncertainties in the velocity-to-depth conversion. A comparison of the bed DEM to the raw data shows that largest gridding errors appear in deep bedrock troughs connected to the ice cap's outlet glaciers and where the ice is thicker (Fig.…”

“…S2). These errors are likely caused by uncertainties in the bedrock picks due to cross-track reflections at the subglacial valley walls (Scanlan et al, 2020), a decreased signal-to-noise ratio beneath thicker ice, and a rapidly changing bed topography over short distances not captured in our grid.…”

Reply on RC1

“…The bed return from the SRH1 data is identified using a semi-automated picking algorithm, which locates the maximum bed reflection power within manually-defined depth boundaries. Over the steep valley walls of bedrock trough T2, the basal reflection was discriminated from cross-track clutter using the results from Scanlan et al (2020). Travel times were then converted to depths using a radar wave velocity in ice of 168.4 m/µs.…”