During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the ‘two-parameter’ technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The ‘Self-Adaptive Two-Parameter’ system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models.
In this paper, we establish the nonlinear signal model of the surface-based frequency-modulated continuous wave (FMCW) ice-sounding radar, and propose a novel range processing strategy which aims at removing the frequency ramp nonlinearity effectively. The proposed algorithm takes fully consideration of the dependence of nonlinearity on the round-trip delay time while providing certain robustness of noise. The theory analysis and implementation steps of the proposed algorithm are demonstrated. The full-scale simulations with different kinds of nonlinearities and various signal-to-noise ratios (SNRs) verify the effectiveness and robustness of the proposed algorithm. We also apply the proposed method to real data set collected during the 31 st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33. The result shows the effectiveness of our algorithm on illustrating clarified Internal Reflecting Horizons (IRHs) of ice sheets. Compared with the echograms processed by the typical range processing scheme, our algorithm performs better in nonlinearity elimination.
In this paper, we calculated the roughness of the basal boundary collected at Princess Elizabeth Land (PEL) to evaluate the topographic structure via the ice-sounding data collected during 32nd and 33rd Chinese Antarctic Research Expeditions (CHINARE 32 and 33). The calculation is achieved by a two-parameter roughness index method, which could differentiate different classes of subglacial landscape, in particular between erosional and depositional settings. Finally, the calculation results of partial regions of PEL are illustrated to describe the roughness of the detected regions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.